R Proyecto Final
#Modificar variable para especificar directorio del Proyecto Final
#local.path <- "/Users/akcasill/Downloads"
user.path <- "/Users/jos/Downloads/"
local.path <- paste(user.path ,"mcc-ad/data",sep = "")
local.path.imgs <- paste(user.path ,"mcc-ad/imgs",sep = "")#Dependencies
#install.packages("png")
library(png)#ASISTENCIAS TOTALES
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.# son 9 semestres de 6 materias cada uno.
# 1.- Asistencias Totales
load("AsistenciasTotales.R")
class(asistencias.totales)[1] "list"length(asistencias.totales)[1] 1000class(asistencias.totales[[1]])[1] "matrix"dim(asistencias.totales[[1]])[1] 32 54class(asistencias.totales[1])[1] "list"asistencias.totales[[1]][1:10,1:10] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] 2 2 2 2 2 2 1 2 2 2
[2,] 2 2 2 2 2 0 2 2 2 2
[3,] 2 2 2 2 2 2 2 0 2 2
[4,] 2 2 2 2 2 2 2 0 2 2
[5,] 2 1 2 1 2 1 2 2 2 2
[6,] 2 1 2 2 2 0 0 0 2 2
[7,] 1 2 2 1 2 0 2 2 2 2
[8,] 2 2 2 0 2 1 1 1 2 2
[9,] 2 2 2 0 1 2 2 0 2 2
[10,] 2 2 2 2 2 2 2 0 0 2#Asistencias
#===================
#Definición Valores
#===================
# 2 El alumno tiene asistnecia completa.
# 1 El alumno tiene retardo.
# 0 El alumno tiene falta.
#Sólo tomar las primeras 12 materias (Columnas)
for(i in 1:length(asistencias.totales)){
asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
}
asistencias.totales[[4]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 2 2 2 2 2 2 0 2 2 2 2 2
[2,] 2 2 2 2 2 1 2 2 2 2 2 2
[3,] 2 2 2 2 0 2 2 1 2 2 2 1
[4,] 2 2 2 2 2 2 2 2 2 2 2 2
[5,] 2 1 2 2 2 2 2 2 2 2 2 2
[6,] 2 1 2 2 1 1 2 1 2 2 1 2
[7,] 1 2 2 2 2 1 2 2 2 2 2 2
[8,] 2 2 2 2 1 2 1 0 2 2 2 2
[9,] 2 2 2 1 0 2 2 1 0 2 2 2
[10,] 2 2 2 2 2 2 2 2 1 2 2 2
[11,] 2 2 2 2 2 2 2 2 2 2 2 2
[12,] 2 2 2 2 2 2 2 2 2 2 2 2
[13,] 2 2 2 2 2 2 2 2 1 2 2 2
[14,] 2 2 2 1 2 2 2 2 2 2 2 2
[15,] 2 2 2 2 2 2 2 2 2 2 2 2
[16,] 2 2 2 2 2 2 2 0 2 2 2 2
[17,] 2 1 2 1 2 2 2 2 2 2 2 2
[18,] 0 2 2 2 1 2 2 2 1 2 2 2
[19,] 2 2 2 1 2 2 0 2 2 2 2 2
[20,] 2 2 2 2 1 2 2 2 2 2 2 2
[21,] 1 2 2 2 1 1 2 2 1 2 2 2
[22,] 2 2 2 0 1 2 2 2 2 2 2 2
[23,] 2 2 2 2 2 2 0 2 2 2 2 2
[24,] 2 2 2 2 2 2 2 2 2 2 2 2
[25,] 2 2 2 2 2 2 0 2 2 2 2 2
[26,] 2 2 2 2 2 2 2 0 1 2 2 2
[27,] 2 2 2 2 2 2 2 2 2 2 2 2
[28,] 2 2 2 2 0 2 2 2 2 2 2 2
[29,] 2 1 2 2 2 2 1 2 1 2 2 2
[30,] 2 2 2 2 0 2 2 1 0 2 2 2
[31,] 2 2 2 2 2 2 2 2 2 2 2 2
[32,] 2 2 2 2 2 2 2 2 1 2 2 2Tamaño de Lista de Asistencia de Alumnos:
length(asistencias.totales)[1] 1000Lista de total Asistencias por Alumno
#
#for(i in 1:length(asistencias.totales)){
# asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
#}
asistencias.alumnos <- matrix(1:12000, nrow=1000, ncol=12)
for(i in 1:length(asistencias.totales)){
for(j in 1:12){
asistencias.alumnos[i,j] <- sum(asistencias.totales[[i]][,j])/32
}
}
asistencias.alumnos[1,] [1] 1.87500 1.87500 2.00000 1.40625 1.90625 1.43750 1.59375 1.12500 1.84375 2.00000 1.96875 1.96875asistencias.df <- as.data.frame(asistencias.alumnos)
#Asistencia Materias Ejemplo: AM1 = Asistencia Materia 1
colnames(asistencias.df) <- c('AM1','AM2','AM3','AM4','AM5','AM6','AM7','AM8','AM9','AM10','AM11','AM12')
#DATA FRAME DE ASISTENCIAS ALUMNOS
#=================================
#Suma de asistencias por Materia
#=================================
asistencias.df#PERFIL ALUMNOS
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.print("Summary")[1] "Summary"load("perfilAlumnos.R")
#head(perfil.alumnos,1)
str(perfil.alumnos)'data.frame': 1000 obs. of 7 variables:
$ genero : int 2 2 2 1 2 2 2 2 1 2 ...
$ admision.letras : num 60.1 59.1 53.1 57 61.5 ...
$ admision.numeros : num 35.2 33.2 21.3 29 37.9 ...
$ promedio.preparatoria : num 70.3 67.2 60 61 74.4 ...
$ edad.ingreso : num 18 17 15 16 18 18 15 17 14 17 ...
$ evalucion.socioeconomica: int 4 4 4 4 4 4 4 4 4 4 ...
$ nota.conducta : num 16 15 13 14 16 16 13 15 12 15 ...summary(perfil.alumnos) genero admision.letras admision.numeros promedio.preparatoria edad.ingreso evalucion.socioeconomica nota.conducta
Min. :1.000 Min. :44.94 Min. : 4.878 Min. : 60.00 Min. :11.00 Min. :1.000 Min. : 9.00
1st Qu.:1.000 1st Qu.:56.61 1st Qu.:28.226 1st Qu.: 60.00 1st Qu.:16.00 1st Qu.:3.000 1st Qu.:14.00
Median :2.000 Median :59.98 Median :34.970 Median : 69.95 Median :17.00 Median :4.000 Median :15.00
Mean :1.595 Mean :60.06 Mean :35.114 Mean : 72.25 Mean :17.53 Mean :3.466 Mean :15.53
3rd Qu.:2.000 3rd Qu.:63.64 3rd Qu.:42.275 3rd Qu.: 80.91 3rd Qu.:19.00 3rd Qu.:4.000 3rd Qu.:17.00
Max. :2.000 Max. :77.71 Max. :70.411 Max. :100.00 Max. :25.00 Max. :4.000 Max. :20.00 #===================
#Definición Valores
#===================
# Genero: 2 Hombre, 1 Mujer.
# admision.letras: Calificación Examen Admisión Español
# admision.numeros: Calificación Examen Admisión Matemáticas
# promedio.preparatoria: Calificación Promedio Preparatoria
# edad.ingreso: Edad, variable numérica
# evalucion.socioeconomica: 1 más privilegiado, 4 menos privilagiado
# nota.conducta: Calificación subjetiva.
perfil.alumnos$genero <- factor(perfil.alumnos$genero)
perfil.alumnos$evalucion.socioeconomica <-
factor(perfil.alumnos$evalucion.socioeconomica)
perfil.alumnos$edad.ingreso <-
factor(perfil.alumnos$edad.ingreso)#DATAFRAME CALIFICACIONES ALUMNOS
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.print("Summary")[1] "Summary"# 3 1000 matrices de 2 x 54, calificación entre 1 y 20
load("ResultadosExamenes.R")
#resultados.examenes.totales
examenes.alumnos <- matrix(1:12000, nrow=1000, ncol=12)
for(i in 1:length(resultados.examenes.totales)){
for(j in 1:12){
examenes.alumnos[i,j] <- sum(resultados.examenes.totales[[i]][,j])/2
}
}
examenes.alumnos[1,] [1] 11.956449 12.330884 12.463337 15.189492 12.328150 17.087821 9.466637 12.011178 11.368753 12.221370 11.416652 12.330704cal.alumnos.df <- as.data.frame(examenes.alumnos)
#Calificaciones Materias Ejemplo: CM2 = Calificiación Promedio Materia 2
colnames(cal.alumnos.df) <- c('CM1','CM2','CM3','CM4','CM5','CM6','CM7','CM8','CM9','CM10','CM11','CM12')
#===================
#Definición Valores
#===================
# CM1: Calificación Materia 1 valor Máximo 20
cal.alumnos.df#TRABAJOS POR CLASE
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.print("Summary")[1] "Summary"# 4 1000 matrices de 4 x 54, son 4 trabajos por clase, entre 1 y 20
load("ResultadoTrabajos.R")
resultados.trabajos.totales[[2]][,1][1] 11.79653 12.11637 12.71856 13.72462tareas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)
for(i in 1:length(resultados.trabajos.totales)){
for(j in 1:12){
tareas.alumnos[i,j] <- sum(resultados.trabajos.totales[[i]][,j])/4
}
}
#tareas.alumnos[1,]
tareas.alumnos.df <- as.data.frame(tareas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(tareas.alumnos.df) <- c('TM1','TM2','TM3','TM4','TM5','TM6','TM7','TM8','TM9','TM10','TM11','TM12')
#===================
#Definición Valores
#===================
# TM1: Calificación Tarea Materia 1 valor Máximo 20
tareas.alumnos.df#VISITAS BIBLIOTECA
# 5 Redondear. Uso fÃsico y virtual. vector. 1000 Matrices, número de veces que asistio a la biblioteca por materia
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.load("UsoBiblioteca.R")
length(uso.biblioteca.totales)[1] 1000mi.val <- uso.biblioteca.totales[[1]][1,1]
mi.val[1] 12.65509mi.val <- as.data.frame(mi.val)
mi.val
visitas.biblio.alumnos <- matrix(1:12000, nrow=1000, ncol=12)
for(i in 1:1000){
for(j in 1:12){
visitas.biblio.alumnos[i,j] <- uso.biblioteca.totales[[i]][1,j]
}
}
visitas.biblio.alumnos.df <- as.data.frame(visitas.biblio.alumnos)
#Visitas Biblioteca Ejemplo: VBM2 = Visitas Biblioteca Materia 2
colnames(visitas.biblio.alumnos.df) <- c('VBM1','VBM2','VBM3','VBM4','VBM5','VBM6','VBM7','VBM8','VBM9','VBM10','VBM11','VBM12')
#===================
#Definición Valores
#===================
# VBM1: Visitas Biblioteca Materia 1
visitas.biblio.alumnos.dfNA
NA#USO DE PLATAFORMAS DIGITALES
# 6 Redondear, vector. Uso de Canvas o de Plataforma digital.
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.load("UsoPlataforma.R")
#uso.plataforma.totales
uso.plataforma.totales[[1]][,1:12] [1] 32.796526 32.554647 32.504125 79.290015 32.600643 80.313415 5.944546 33.398886 32.664804 33.522435 32.831749 32.208083plataformas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)
for(i in 1:1000){
for(j in 1:12){
plataformas.alumnos[i,j] <- uso.plataforma.totales[[i]][1,j]
}
}
#tareas.alumnos[1,]
plataformas.alumnos.df <- as.data.frame(plataformas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(plataformas.alumnos.df) <- c('PDM1','PDM2','PDM3','PDM4','PDM5','PDM6','PDM7','PDM8','PDM9','PDM10','PDM11','PDM12')
#===================
#Definición Valores
#===================
# PDM1: Plataformas Digitales Materia 1 valor Máximo 20
plataformas.alumnos.dfNA#APARTADO DE LIBROS POR MATERIA
# 7
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.load("ApartadoDeLibros.R") #1000 matrices, cantidad de libros que el alumno reservó por materia.
separacion.libros.totales[[1]][,1:12] [1] 1 1 1 3 1 3 0 1 1 1 1 1reserva.libros.alumnos <- matrix(1:12000, nrow=1000, ncol=12)
for(i in 1:1000){
for(j in 1:12){
reserva.libros.alumnos[i,j] <- separacion.libros.totales[[i]][1,j]
}
}
reserva.libros.alumnos.df <- as.data.frame(reserva.libros.alumnos)
#Reserva de Libris Ejemplo: RLM2 = Reserva de Libros Por Materia 2
colnames(reserva.libros.alumnos.df) <- c('RLM1','RLM2','RLM3','RLM4','RLM5','RLM6','RLM7','RLM8','RLM9','RLM10','RLM11','RLM12')
#===================
#Definición Valores
#===================
# RLM1: Reserva de Libros pro Materia 1
reserva.libros.alumnos.dfNA#DISTRIBUCIÓN DE BECAS ALUMNOS
# 8 vector binario, 1 tiene beca, 0 no tiene Beca
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.load("Becas.R")
distribucion.becas[[1]][1] 0sum(distribucion.becas)[1] 163becas.alumnos <- matrix(1:1000, nrow=1000, ncol=1)
for(i in 1:1000){
becas.alumnos[i] <- distribucion.becas[i]
}
becas.alumnos.df <- as.data.frame(becas.alumnos)
colnames(becas.alumnos.df) <- c('BECA')
#===================
#Definición Valores
#===================
# BECA: Tiene Beca 1
becas.alumnos.df#Necesita ser un factor por que solo tiene dos valores 0 o 1
becas.alumnos.df$BECA <- as.factor(becas.alumnos.df$BECA)
becas.alumnos.df#HISTORIAL DE PAGOS ALUMNOS
# 9 2 en tiempo, 1 retraso, 0, Son 9 semestres pero hay que user sólo 2 primeras columnas, 4 pagos.
setwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.load("HistorialPagos.R")
length(registro.pagos)[1] 1000registro.pagos[[500]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 2 2 2 2 2 2 1 2 2
[2,] 2 1 1 2 2 2 2 2 2
[3,] 2 2 2 2 1 2 2 1 2
[4,] 1 2 2 2 2 2 2 1 2pagos.alumnos <- matrix(1:2000, nrow=1000, ncol=2)
for(i in 1:1000){
for(j in 1:2){
pagos.alumnos[i,j] <- sum(registro.pagos[[i]][,j])/4
}
}
#tareas.alumnos[1,]
pagos.alumnos.df <- as.data.frame(pagos.alumnos)
#Pago Semestre: PSEM2 = Pago Semestre 2
colnames(pagos.alumnos.df) <- c('PSEM1','PSEM2')
#===================
#Definición Valores
#===================
# PSEM1: Suma de pagos semestre 1, 2 valor máximo.
pagos.alumnos.dfNAdatos.alumnos.df <- cbind.data.frame(perfil.alumnos,
becas.alumnos.df,
asistencias.df,
cal.alumnos.df,
tareas.alumnos.df,
visitas.biblio.alumnos.df,
plataformas.alumnos.df,
reserva.libros.alumnos.df,
pagos.alumnos.df)
datos.alumnos.dfNAstr(datos.alumnos.df)'data.frame': 1000 obs. of 82 variables:
$ genero : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 2 1 2 ...
$ admision.letras : num 60.1 59.1 53.1 57 61.5 ...
$ admision.numeros : num 35.2 33.2 21.3 29 37.9 ...
$ promedio.preparatoria : num 70.3 67.2 60 61 74.4 ...
$ edad.ingreso : Factor w/ 15 levels "11","12","13",..: 8 7 5 6 8 8 5 7 4 7 ...
$ evalucion.socioeconomica: Factor w/ 4 levels "1","2","3","4": 4 4 4 4 4 4 4 4 4 4 ...
$ nota.conducta : num 16 15 13 14 16 16 13 15 12 15 ...
$ BECA : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ AM1 : num 1.88 1.88 1.88 1.88 1.88 ...
$ AM2 : num 1.88 1.41 1.41 1.88 1.41 ...
$ AM3 : num 2 2 2 2 1.72 ...
$ AM4 : num 1.41 1.81 1.81 1.81 1.81 ...
$ AM5 : num 1.91 1.5 1.38 1.56 1.91 ...
$ AM6 : num 1.44 1.38 1.12 1.88 1.12 ...
$ AM7 : num 1.59 1.69 1.69 1.69 1.69 ...
$ AM8 : num 1.12 1.12 1.94 1.69 1.12 ...
$ AM9 : num 1.84 1.84 1.84 1.66 1.66 ...
$ AM10 : num 2 2 1.22 2 2 ...
$ AM11 : num 1.97 1.97 1.97 1.97 1.97 ...
$ AM12 : num 1.97 1.97 1.97 1.97 1.97 ...
$ CM1 : num 12 12 12 12 12 ...
$ CM2 : num 12.3 12.3 15.8 15.8 12.3 ...
$ CM3 : num 12.5 12.5 12.5 16 16 ...
$ CM4 : num 15.2 11.9 11.9 15.2 11.9 ...
$ CM5 : num 12.3 15.8 12.3 12.3 12.3 ...
$ CM6 : num 17.1 17.1 13.3 17.1 13.3 ...
$ CM7 : num 9.47 13.08 13.08 13.08 13.08 ...
$ CM8 : num 12.01 15.35 12.01 7.69 15.35 ...
$ CM9 : num 11.37 11.37 11.37 18.25 6.61 ...
$ CM10 : num 12.2 12.2 12.2 15.6 12.2 ...
$ CM11 : num 11.4 11.4 11.4 11.4 11.4 ...
$ CM12 : num 12.3 12.3 12.3 15.8 12.3 ...
$ TM1 : num 12.6 12.6 12.6 12.6 12.6 ...
$ TM2 : num 12.2 12.2 15.6 15.6 12.2 ...
$ TM3 : num 12.3 12.3 12.3 15.7 15.7 ...
$ TM4 : num 15.2 11.9 11.9 15.2 11.9 ...
$ TM5 : num 12.6 16.1 12.6 12.6 12.6 ...
$ TM6 : num 16.2 16.2 12.6 16.2 12.6 ...
$ TM7 : num 7.97 12.18 12.18 12.18 12.18 ...
$ TM8 : num 12.59 16.13 12.59 8.66 16.13 ...
$ TM9 : num 11.5 11.5 11.5 18.33 6.84 ...
$ TM10 : num 12.5 12.5 12.5 15.9 12.5 ...
$ TM11 : num 11.6 11.6 11.6 11.6 11.6 ...
$ TM12 : num 12.6 12.6 12.6 16.1 12.6 ...
$ VBM1 : num 12.7 12.7 12.7 12.7 12.7 ...
$ VBM2 : num 11.8 11.8 27.8 27.8 11.8 ...
$ VBM3 : num 11.7 11.7 11.7 27.5 27.5 ...
$ VBM4 : num 33.8 15.9 15.9 33.8 15.9 ...
$ VBM5 : num 12 28 12 12 12 ...
$ VBM6 : num 34.1 34.1 16.1 34.1 16.1 ...
$ VBM7 : num 2.98 19.89 19.89 19.89 19.89 ...
$ VBM8 : num 14.66 31.99 14.66 1.93 31.99 ...
$ VBM9 : num 12.22 12.22 12.22 62.22 1.44 ...
$ VBM10 : num 15.1 15.1 15.1 32.6 15.1 ...
$ VBM11 : num 12.8 12.8 12.8 12.8 12.8 ...
$ VBM12 : num 10.7 10.7 10.7 26 10.7 ...
$ PDM1 : num 32.8 32.8 32.8 32.8 32.8 ...
$ PDM2 : num 32.6 32.6 59.2 59.2 32.6 ...
$ PDM3 : num 32.5 32.5 32.5 58.4 58.4 ...
$ PDM4 : num 79.3 33.8 33.8 79.3 33.8 ...
$ PDM5 : num 32.6 60 32.6 32.6 32.6 ...
$ PDM6 : num 80.3 80.3 33.8 80.3 33.8 ...
$ PDM7 : num 5.94 34.97 34.97 34.97 34.97 ...
$ PDM8 : num 33.4 73.31 33.4 3.33 73.31 ...
$ PDM9 : num 32.66 32.66 32.66 161.08 2.11 ...
$ PDM10 : num 33.5 33.5 33.5 75.4 33.5 ...
$ PDM11 : num 32.8 32.8 32.8 32.8 32.8 ...
$ PDM12 : num 32.2 32.2 32.2 53.5 32.2 ...
$ RLM1 : num 1 1 1 1 1 1 4 2 1 1 ...
$ RLM2 : num 1 1 2 2 1 0 2 1 1 2 ...
$ RLM3 : num 1 1 1 2 2 2 2 2 1 2 ...
$ RLM4 : num 3 1 1 3 1 3 3 3 1 1 ...
$ RLM5 : num 1 2 1 1 1 1 2 1 1 2 ...
$ RLM6 : num 3 3 1 3 1 5 0 3 1 3 ...
$ RLM7 : num 0 1 1 1 1 5 3 1 1 3 ...
$ RLM8 : num 1 2 1 0 2 1 4 0 1 2 ...
$ RLM9 : num 1 1 1 4 0 1 2 2 1 1 ...
$ RLM10 : num 1 1 1 3 1 3 3 3 5 3 ...
$ RLM11 : num 1 1 1 1 1 1 2 1 1 1 ...
$ RLM12 : num 1 1 1 2 1 0 2 1 1 1 ...
$ PSEM1 : num 2 2 2 2 2 2 1.5 2 2 2 ...
$ PSEM2 : num 2 1.5 1.5 2 2 1.5 2 2 2 2 ...summary(datos.alumnos.df) genero admision.letras admision.numeros promedio.preparatoria edad.ingreso evalucion.socioeconomica nota.conducta BECA
1:405 Min. :44.94 Min. : 4.878 Min. : 60.00 17 :200 1: 56 Min. : 9.00 0:837
2:595 1st Qu.:56.61 1st Qu.:28.226 1st Qu.: 60.00 18 :167 2:107 1st Qu.:14.00 1:163
Median :59.98 Median :34.970 Median : 69.95 19 :166 3:152 Median :15.00
Mean :60.06 Mean :35.114 Mean : 72.25 16 :140 4:685 Mean :15.53
3rd Qu.:63.64 3rd Qu.:42.275 3rd Qu.: 80.91 20 :113 3rd Qu.:17.00
Max. :77.71 Max. :70.411 Max. :100.00 15 : 98 Max. :20.00
(Other):116
AM1 AM2 AM3 AM4 AM5 AM6 AM7 AM8
Min. :1.875 Min. :1.406 Min. :1.031 Min. :1.031 Min. :1.375 Min. :1.125 Min. :1.406 Min. :1.125
1st Qu.:1.875 1st Qu.:1.875 1st Qu.:1.719 1st Qu.:1.406 1st Qu.:1.562 1st Qu.:1.438 1st Qu.:1.594 1st Qu.:1.875
Median :1.875 Median :1.875 Median :2.000 Median :1.812 Median :1.906 Median :1.875 Median :1.688 Median :1.938
Mean :1.875 Mean :1.779 Mean :1.837 Mean :1.658 Mean :1.794 Mean :1.720 Mean :1.632 Mean :1.801
3rd Qu.:1.875 3rd Qu.:1.875 3rd Qu.:2.000 3rd Qu.:1.812 3rd Qu.:1.906 3rd Qu.:1.875 3rd Qu.:1.688 3rd Qu.:1.938
Max. :1.875 Max. :1.875 Max. :2.000 Max. :1.812 Max. :1.906 Max. :1.875 Max. :1.688 Max. :1.938
AM9 AM10 AM11 AM12 CM1 CM2 CM3 CM4
Min. :1.562 Min. :1.219 Min. :1.562 Min. :1.438 Min. : 7.594 Min. : 8.218 Min. : 8.439 Min. : 7.487
1st Qu.:1.797 1st Qu.:2.000 1st Qu.:1.969 1st Qu.:1.969 1st Qu.:11.956 1st Qu.:12.331 1st Qu.:12.463 1st Qu.:11.892
Median :1.844 Median :2.000 Median :1.969 Median :1.969 Median :11.956 Median :12.331 Median :12.463 Median :11.892
Mean :1.781 Mean :1.872 Mean :1.892 Mean :1.875 Mean :12.751 Mean :13.567 Mean :13.701 Mean :13.064
3rd Qu.:1.844 3rd Qu.:2.000 3rd Qu.:1.969 3rd Qu.:1.969 3rd Qu.:11.956 3rd Qu.:15.775 3rd Qu.:15.951 3rd Qu.:15.189
Max. :1.844 Max. :2.000 Max. :1.969 Max. :1.969 Max. :18.638 Max. :18.887 Max. :18.976 Max. :18.595
CM5 CM6 CM7 CM8 CM9 CM10 CM11 CM12
Min. : 8.214 Min. : 9.86 Min. : 9.467 Min. : 7.685 Min. : 6.615 Min. : 8.036 Min. : 6.694 Min. : 8.218
1st Qu.:12.328 1st Qu.:13.32 1st Qu.:13.080 1st Qu.:12.011 1st Qu.:11.369 1st Qu.:12.221 1st Qu.:11.417 1st Qu.:12.331
Median :12.328 Median :13.32 Median :13.080 Median :12.011 Median :11.369 Median :12.221 Median :11.417 Median :12.331
Mean :13.582 Mean :14.72 Mean :14.600 Mean :13.233 Mean :12.493 Mean :13.488 Mean :12.466 Mean :13.539
3rd Qu.:15.771 3rd Qu.:17.09 3rd Qu.:16.773 3rd Qu.:15.348 3rd Qu.:14.492 3rd Qu.:15.628 3rd Qu.:14.556 3rd Qu.:15.774
Max. :18.885 Max. :19.54 Max. :19.387 Max. :18.674 Max. :18.246 Max. :18.814 Max. :18.278 Max. :18.887
TM1 TM2 TM3 TM4 TM5 TM6 TM7 TM8
Min. : 8.648 Min. : 8.036 Min. : 8.11 Min. : 7.457 Min. : 8.608 Min. : 8.735 Min. : 7.965 Min. : 8.657
1st Qu.:12.589 1st Qu.:12.221 1st Qu.:12.27 1st Qu.:11.874 1st Qu.:12.565 1st Qu.:12.641 1st Qu.:12.179 1st Qu.:12.594
Median :12.589 Median :12.221 Median :12.27 Median :11.874 Median :12.565 Median :12.641 Median :12.179 Median :12.594
Mean :13.375 Mean :13.437 Mean :13.47 Mean :13.043 Mean :13.865 Mean :13.931 Mean :13.539 Mean :13.920
3rd Qu.:12.589 3rd Qu.:15.629 3rd Qu.:15.69 3rd Qu.:15.166 3rd Qu.:16.087 3rd Qu.:16.188 3rd Qu.:15.572 3rd Qu.:16.126
Max. :19.059 Max. :18.814 Max. :18.84 Max. :18.583 Max. :19.043 Max. :19.094 Max. :18.786 Max. :19.063
TM9 TM10 TM11 TM12 VBM1 VBM2 VBM3 VBM4
Min. : 6.836 Min. : 8.418 Min. : 7.003 Min. : 8.624 Min. : 1.531 Min. : 1.37 Min. : 1.336 Min. : 2.172
1st Qu.:11.502 1st Qu.:12.451 1st Qu.:11.602 1st Qu.:12.574 1st Qu.:12.655 1st Qu.:11.85 1st Qu.:11.680 1st Qu.:15.858
Median :11.502 Median :12.451 Median :11.602 Median :12.574 Median :12.655 Median :11.85 Median :11.680 Median :15.858
Mean :12.650 Mean :13.758 Mean :12.688 Mean :13.829 Mean :19.024 Mean :19.30 Mean :19.149 Mean :23.968
3rd Qu.:14.669 3rd Qu.:15.934 3rd Qu.:14.802 3rd Qu.:16.099 3rd Qu.:12.655 3rd Qu.:27.77 3rd Qu.:27.521 3rd Qu.:33.787
Max. :18.334 Max. :18.967 Max. :18.401 Max. :19.050 Max. :62.655 Max. :61.85 Max. :61.680 Max. :65.858
VBM5 VBM6 VBM7 VBM8 VBM9 VBM10 VBM11 VBM12
Min. : 1.40 Min. : 2.213 Min. : 2.978 Min. : 1.933 Min. : 1.443 Min. : 2.015 Min. : 1.554 Min. : 1.139
1st Qu.:12.00 1st Qu.:16.063 1st Qu.:19.889 1st Qu.:14.663 1st Qu.:12.216 1st Qu.:15.075 1st Qu.:12.773 1st Qu.:10.694
Median :12.00 Median :16.063 Median :19.889 Median :14.663 Median :12.216 Median :15.075 Median :12.773 Median :10.694
Mean :19.66 Mean :24.112 Mean :29.373 Mean :22.640 Mean :19.961 Mean :23.304 Mean :20.444 Mean :17.920
3rd Qu.:28.00 3rd Qu.:34.094 3rd Qu.:39.834 3rd Qu.:31.994 3rd Qu.:28.324 3rd Qu.:32.612 3rd Qu.:29.159 3rd Qu.:26.040
Max. :62.00 Max. :66.063 Max. :69.889 Max. :64.663 Max. :62.216 Max. :65.075 Max. :62.773 Max. :60.694
PDM1 PDM2 PDM3 PDM4 PDM5 PDM6 PDM7
Min. : 2.328 Min. : 1.924 Min. : 1.84 Min. : 3.929 Min. : 2.001 Min. : 4.031 Min. : 5.945
1st Qu.: 32.797 1st Qu.: 32.555 1st Qu.: 32.50 1st Qu.: 33.757 1st Qu.: 32.601 1st Qu.: 33.819 1st Qu.: 34.967
Median : 32.797 Median : 32.555 Median : 32.50 Median : 33.757 Median : 32.601 Median : 33.819 Median : 34.967
Mean : 49.085 Mean : 45.384 Mean : 45.65 Mean : 55.804 Mean : 46.282 Mean : 56.079 Mean : 68.347
3rd Qu.: 32.797 3rd Qu.: 59.244 3rd Qu.: 58.40 3rd Qu.: 79.290 3rd Qu.: 60.011 3rd Qu.: 80.313 3rd Qu.: 99.445
Max. :163.275 Max. :159.244 Max. :158.40 Max. :179.290 Max. :160.011 Max. :180.313 Max. :199.445
PDM8 PDM9 PDM10 PDM11 PDM12 RLM1 RLM2 RLM3
Min. : 3.331 Min. : 2.108 Min. : 3.537 Min. : 2.386 Min. : 1.347 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.: 33.399 1st Qu.: 32.665 1st Qu.: 33.522 1st Qu.: 32.832 1st Qu.: 32.208 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median : 33.399 Median : 32.665 Median : 33.522 Median : 32.832 Median : 32.208 Median :1.000 Median :1.000 Median :1.000
Mean : 52.866 Mean : 47.051 Mean : 54.494 Mean : 47.882 Mean : 42.551 Mean :1.373 Mean :1.425 Mean :1.429
3rd Qu.: 73.315 3rd Qu.: 61.080 3rd Qu.: 75.374 3rd Qu.: 63.862 3rd Qu.: 53.468 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :173.315 Max. :161.080 Max. :175.374 Max. :163.862 Max. :153.468 Max. :4.000 Max. :4.000 Max. :4.000
RLM4 RLM5 RLM6 RLM7 RLM8 RLM9 RLM10 RLM11
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000 Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean :1.878 Mean :1.436 Mean :1.866 Mean :1.955 Mean :1.441 Mean :1.445 Mean :1.895 Mean :1.429
3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000
Max. :5.000 Max. :4.000 Max. :5.000 Max. :5.000 Max. :4.000 Max. :4.000 Max. :5.000 Max. :4.000
RLM12 PSEM1 PSEM2
Min. :0.000 Min. :1.500 Min. :1.50
1st Qu.:1.000 1st Qu.:1.750 1st Qu.:1.75
Median :1.000 Median :2.000 Median :2.00
Mean :1.419 Mean :1.892 Mean :1.90
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00
Max. :4.000 Max. :2.000 Max. :2.00
datos.integrados <- datos.alumnos.dfsetwd(local.path)The working directory was changed to /Users/jos/Downloads/mcc-ad/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.save(datos.integrados, file="datos.integrados.R")
getwd()[1] "/Users/jos/Downloads/mcc-ad/data"load("datos.integrados.R")
datos.integradoshead(datos.integrados)NA#Separar 100 alumnos que no entraran en Kmeans
set.seed(1234)
ind <- sample(x=c(0,1),size=nrow(datos.integrados),
replace=TRUE,prob = c(0.9,0.1))
ind [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[68] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0
[135] 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
[202] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[269] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0
[336] 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0
[403] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[470] 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
[537] 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
[604] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0
[671] 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0
[738] 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[805] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1
[872] 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
[939] 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0alumnos.nuevos <- datos.integrados[ind==1,]
alumnos.actuales <- datos.integrados[ind==0,]
alumnos.nuevosalumnos.actualessummary(alumnos.nuevos) genero admision.letras admision.numeros promedio.preparatoria edad.ingreso evalucion.socioeconomica nota.conducta BECA AM1
1:45 Min. :44.94 Min. : 4.878 Min. : 60.00 17 :28 1: 6 Min. : 9.00 0:99 Min. :1.875
2:75 1st Qu.:57.09 1st Qu.:29.176 1st Qu.: 61.26 19 :26 2:15 1st Qu.:14.00 1:21 1st Qu.:1.875
Median :59.97 Median :34.947 Median : 69.92 18 :16 3:16 Median :15.00 Median :1.875
Mean :60.08 Mean :35.167 Mean : 72.31 16 :15 4:83 Mean :15.52 Mean :1.875
3rd Qu.:63.28 3rd Qu.:41.556 3rd Qu.: 79.83 20 :13 3rd Qu.:17.00 3rd Qu.:1.875
Max. :72.00 Max. :58.992 Max. :100.00 15 :12 Max. :20.00 Max. :1.875
(Other):10
AM2 AM3 AM4 AM5 AM6 AM7 AM8 AM9
Min. :1.406 Min. :1.031 Min. :1.031 Min. :1.375 Min. :1.125 Min. :1.406 Min. :1.125 Min. :1.562
1st Qu.:1.875 1st Qu.:1.719 1st Qu.:1.812 1st Qu.:1.906 1st Qu.:1.875 1st Qu.:1.688 1st Qu.:1.938 1st Qu.:1.797
Median :1.875 Median :2.000 Median :1.812 Median :1.906 Median :1.875 Median :1.688 Median :1.938 Median :1.844
Mean :1.786 Mean :1.847 Mean :1.670 Mean :1.808 Mean :1.739 Mean :1.641 Mean :1.825 Mean :1.778
3rd Qu.:1.875 3rd Qu.:2.000 3rd Qu.:1.812 3rd Qu.:1.906 3rd Qu.:1.875 3rd Qu.:1.688 3rd Qu.:1.938 3rd Qu.:1.844
Max. :1.875 Max. :2.000 Max. :1.812 Max. :1.906 Max. :1.875 Max. :1.688 Max. :1.938 Max. :1.844
AM10 AM11 AM12 CM1 CM2 CM3 CM4 CM5
Min. :1.219 Min. :1.562 Min. :1.438 Min. : 7.594 Min. : 8.218 Min. : 8.439 Min. : 7.487 Min. : 8.214
1st Qu.:2.000 1st Qu.:1.781 1st Qu.:1.969 1st Qu.:11.956 1st Qu.:12.331 1st Qu.:12.463 1st Qu.:11.892 1st Qu.:12.328
Median :2.000 Median :1.969 Median :1.969 Median :11.956 Median :12.331 Median :12.463 Median :11.892 Median :12.328
Mean :1.885 Mean :1.892 Mean :1.897 Mean :12.729 Mean :13.579 Mean :13.628 Mean :13.225 Mean :13.702
3rd Qu.:2.000 3rd Qu.:1.969 3rd Qu.:1.969 3rd Qu.:11.956 3rd Qu.:15.775 3rd Qu.:15.951 3rd Qu.:15.189 3rd Qu.:15.771
Max. :2.000 Max. :1.969 Max. :1.969 Max. :18.638 Max. :18.887 Max. :18.976 Max. :18.595 Max. :18.885
CM6 CM7 CM8 CM9 CM10 CM11 CM12 TM1
Min. : 9.86 Min. : 9.467 Min. : 7.685 Min. : 6.615 Min. : 8.036 Min. : 6.694 Min. : 8.218 Min. : 8.648
1st Qu.:13.32 1st Qu.:13.080 1st Qu.:12.011 1st Qu.:11.369 1st Qu.:12.221 1st Qu.:11.417 1st Qu.:12.331 1st Qu.:12.589
Median :15.20 Median :13.080 Median :12.011 Median :11.369 Median :12.221 Median :11.417 Median :12.331 Median :12.589
Mean :15.15 Mean :14.247 Mean :13.263 Mean :12.507 Mean :13.653 Mean :12.172 Mean :13.711 Mean :13.355
3rd Qu.:17.09 3rd Qu.:16.773 3rd Qu.:15.348 3rd Qu.:14.492 3rd Qu.:15.628 3rd Qu.:14.556 3rd Qu.:15.774 3rd Qu.:12.589
Max. :19.54 Max. :19.387 Max. :18.674 Max. :18.246 Max. :18.814 Max. :18.278 Max. :18.887 Max. :19.059
TM2 TM3 TM4 TM5 TM6 TM7 TM8 TM9
Min. : 8.036 Min. : 8.11 Min. : 7.457 Min. : 8.608 Min. : 8.735 Min. : 7.965 Min. : 8.657 Min. : 6.836
1st Qu.:12.221 1st Qu.:12.27 1st Qu.:11.874 1st Qu.:12.565 1st Qu.:12.641 1st Qu.:12.179 1st Qu.:12.594 1st Qu.:11.502
Median :12.221 Median :12.27 Median :11.874 Median :12.565 Median :14.415 Median :12.179 Median :12.594 Median :11.502
Mean :13.448 Mean :13.40 Mean :13.204 Mean :13.986 Mean :14.364 Mean :13.151 Mean :13.943 Mean :12.666
3rd Qu.:15.629 3rd Qu.:15.69 3rd Qu.:15.166 3rd Qu.:16.087 3rd Qu.:16.188 3rd Qu.:15.572 3rd Qu.:16.126 3rd Qu.:14.669
Max. :18.814 Max. :18.84 Max. :18.583 Max. :19.043 Max. :19.094 Max. :18.786 Max. :19.063 Max. :18.334
TM10 TM11 TM12 VBM1 VBM2 VBM3 VBM4 VBM5
Min. : 8.418 Min. : 7.003 Min. : 8.624 Min. : 1.531 Min. : 1.37 Min. : 1.336 Min. : 2.172 Min. : 1.40
1st Qu.:12.451 1st Qu.:11.602 1st Qu.:12.574 1st Qu.:12.655 1st Qu.:11.85 1st Qu.:11.680 1st Qu.:15.858 1st Qu.:12.00
Median :12.451 Median :11.602 Median :12.574 Median :12.655 Median :11.85 Median :11.680 Median :15.858 Median :12.00
Mean :13.918 Mean :12.397 Mean :14.001 Mean :18.942 Mean :19.67 Mean :19.901 Mean :24.723 Mean :20.29
3rd Qu.:15.934 3rd Qu.:14.802 3rd Qu.:16.099 3rd Qu.:12.655 3rd Qu.:27.77 3rd Qu.:27.521 3rd Qu.:33.787 3rd Qu.:28.00
Max. :18.967 Max. :18.401 Max. :19.050 Max. :62.655 Max. :61.85 Max. :61.680 Max. :65.858 Max. :62.00
VBM6 VBM7 VBM8 VBM9 VBM10 VBM11 VBM12 PDM1
Min. : 2.213 Min. : 2.978 Min. : 1.933 Min. : 1.443 Min. : 2.015 Min. : 1.554 Min. : 1.139 Min. : 2.328
1st Qu.:16.063 1st Qu.:19.889 1st Qu.:14.663 1st Qu.:12.216 1st Qu.:15.075 1st Qu.:12.773 1st Qu.:10.694 1st Qu.: 32.797
Median :25.078 Median :19.889 Median :14.663 Median :12.216 Median :15.075 Median :12.773 Median :10.694 Median : 32.797
Mean :26.704 Mean :27.247 Mean :22.746 Mean :20.068 Mean :24.151 Mean :20.014 Mean :18.761 Mean : 48.853
3rd Qu.:34.094 3rd Qu.:39.834 3rd Qu.:31.994 3rd Qu.:28.324 3rd Qu.:32.612 3rd Qu.:29.159 3rd Qu.:26.040 3rd Qu.: 32.797
Max. :66.063 Max. :69.889 Max. :64.663 Max. :62.216 Max. :65.075 Max. :62.773 Max. :60.694 Max. :163.275
PDM2 PDM3 PDM4 PDM5 PDM6 PDM7 PDM8
Min. : 1.924 Min. : 1.84 Min. : 3.929 Min. : 2.001 Min. : 4.031 Min. : 5.945 Min. : 3.331
1st Qu.: 32.555 1st Qu.: 32.50 1st Qu.: 33.757 1st Qu.: 32.601 1st Qu.: 33.819 1st Qu.: 34.967 1st Qu.: 33.399
Median : 32.555 Median : 32.50 Median : 33.757 Median : 32.601 Median : 57.066 Median : 34.967 Median : 33.399
Mean : 46.181 Mean : 48.16 Mean : 57.651 Mean : 47.534 Mean : 63.165 Mean : 62.721 Mean : 53.190
3rd Qu.: 59.244 3rd Qu.: 58.40 3rd Qu.: 79.290 3rd Qu.: 60.011 3rd Qu.: 80.313 3rd Qu.: 99.445 3rd Qu.: 73.315
Max. :159.244 Max. :158.40 Max. :179.290 Max. :160.011 Max. :180.313 Max. :199.445 Max. :173.315
PDM9 PDM10 PDM11 PDM12 RLM1 RLM2 RLM3 RLM4
Min. : 2.108 Min. : 3.537 Min. : 2.386 Min. : 1.347 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.: 32.665 1st Qu.: 33.522 1st Qu.: 32.832 1st Qu.: 32.208 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median : 32.665 Median : 33.522 Median : 32.832 Median : 32.208 Median :1.000 Median :1.000 Median :1.000 Median :1.000
Mean : 47.036 Mean : 56.773 Mean : 47.359 Mean : 44.220 Mean :1.367 Mean :1.442 Mean :1.458 Mean :1.958
3rd Qu.: 61.080 3rd Qu.: 75.374 3rd Qu.: 63.862 3rd Qu.: 53.468 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000
Max. :161.080 Max. :175.374 Max. :163.862 Max. :153.468 Max. :4.000 Max. :4.000 Max. :4.000 Max. :5.000
RLM5 RLM6 RLM7 RLM8 RLM9 RLM10 RLM11 RLM12 PSEM1
Min. :0.000 Min. :0.000 Min. :0.0 Min. :0.00 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000 Min. :1.50
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.75
Median :1.000 Median :2.000 Median :1.0 Median :1.00 Median :1.00 Median :1.000 Median :1.000 Median :1.000 Median :2.00
Mean :1.475 Mean :2.092 Mean :1.8 Mean :1.45 Mean :1.45 Mean :1.958 Mean :1.383 Mean :1.475 Mean :1.89
3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.0 3rd Qu.:2.00 3rd Qu.:2.00 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.00
Max. :4.000 Max. :5.000 Max. :5.0 Max. :4.00 Max. :4.00 Max. :5.000 Max. :4.000 Max. :4.000 Max. :2.00
PSEM2
Min. :1.500
1st Qu.:1.750
Median :2.000
Mean :1.923
3rd Qu.:2.000
Max. :2.000
summary(alumnos.actuales) genero admision.letras admision.numeros promedio.preparatoria edad.ingreso evalucion.socioeconomica nota.conducta BECA
1:360 Min. :44.99 Min. : 4.986 Min. : 60.00 17 :172 1: 50 Min. : 9.00 0:738
2:520 1st Qu.:56.59 1st Qu.:28.187 1st Qu.: 60.00 18 :151 2: 92 1st Qu.:14.00 1:142
Median :60.04 Median :35.076 Median : 70.11 19 :140 3:136 Median :15.50
Mean :60.05 Mean :35.107 Mean : 72.24 16 :125 4:602 Mean :15.53
3rd Qu.:63.67 3rd Qu.:42.335 3rd Qu.: 81.00 20 :100 3rd Qu.:17.00
Max. :77.71 Max. :70.411 Max. :100.00 15 : 86 Max. :20.00
(Other):106
AM1 AM2 AM3 AM4 AM5 AM6 AM7 AM8
Min. :1.875 Min. :1.406 Min. :1.031 Min. :1.031 Min. :1.375 Min. :1.125 Min. :1.406 Min. :1.125
1st Qu.:1.875 1st Qu.:1.875 1st Qu.:1.719 1st Qu.:1.406 1st Qu.:1.562 1st Qu.:1.438 1st Qu.:1.594 1st Qu.:1.688
Median :1.875 Median :1.875 Median :2.000 Median :1.812 Median :1.906 Median :1.875 Median :1.688 Median :1.938
Mean :1.875 Mean :1.778 Mean :1.836 Mean :1.656 Mean :1.793 Mean :1.717 Mean :1.631 Mean :1.798
3rd Qu.:1.875 3rd Qu.:1.875 3rd Qu.:2.000 3rd Qu.:1.812 3rd Qu.:1.906 3rd Qu.:1.875 3rd Qu.:1.688 3rd Qu.:1.938
Max. :1.875 Max. :1.875 Max. :2.000 Max. :1.812 Max. :1.906 Max. :1.875 Max. :1.688 Max. :1.938
AM9 AM10 AM11 AM12 CM1 CM2 CM3 CM4
Min. :1.562 Min. :1.219 Min. :1.562 Min. :1.438 Min. : 7.594 Min. : 8.218 Min. : 8.439 Min. : 7.487
1st Qu.:1.797 1st Qu.:2.000 1st Qu.:1.969 1st Qu.:1.969 1st Qu.:11.956 1st Qu.:12.331 1st Qu.:12.463 1st Qu.:11.892
Median :1.844 Median :2.000 Median :1.969 Median :1.969 Median :11.956 Median :12.331 Median :12.463 Median :11.892
Mean :1.781 Mean :1.870 Mean :1.892 Mean :1.872 Mean :12.754 Mean :13.566 Mean :13.711 Mean :13.042
3rd Qu.:1.844 3rd Qu.:2.000 3rd Qu.:1.969 3rd Qu.:1.969 3rd Qu.:11.956 3rd Qu.:15.775 3rd Qu.:15.951 3rd Qu.:15.189
Max. :1.844 Max. :2.000 Max. :1.969 Max. :1.969 Max. :18.638 Max. :18.887 Max. :18.976 Max. :18.595
CM5 CM6 CM7 CM8 CM9 CM10 CM11 CM12
Min. : 8.214 Min. : 9.86 Min. : 9.467 Min. : 7.685 Min. : 6.615 Min. : 8.036 Min. : 6.694 Min. : 8.218
1st Qu.:12.328 1st Qu.:13.32 1st Qu.:13.080 1st Qu.:12.011 1st Qu.:11.369 1st Qu.:12.221 1st Qu.:11.417 1st Qu.:12.331
Median :12.328 Median :13.32 Median :13.080 Median :12.011 Median :11.369 Median :12.221 Median :11.417 Median :12.331
Mean :13.566 Mean :14.67 Mean :14.648 Mean :13.229 Mean :12.491 Mean :13.466 Mean :12.506 Mean :13.516
3rd Qu.:15.771 3rd Qu.:17.09 3rd Qu.:16.773 3rd Qu.:15.348 3rd Qu.:14.492 3rd Qu.:15.628 3rd Qu.:14.556 3rd Qu.:15.774
Max. :18.885 Max. :19.54 Max. :19.387 Max. :18.674 Max. :18.246 Max. :18.814 Max. :18.278 Max. :18.887
TM1 TM2 TM3 TM4 TM5 TM6 TM7 TM8
Min. : 8.648 Min. : 8.036 Min. : 8.11 Min. : 7.457 Min. : 8.608 Min. : 8.735 Min. : 7.965 Min. : 8.657
1st Qu.:12.589 1st Qu.:12.221 1st Qu.:12.27 1st Qu.:11.874 1st Qu.:12.565 1st Qu.:12.641 1st Qu.:12.179 1st Qu.:12.594
Median :12.589 Median :12.221 Median :12.27 Median :11.874 Median :12.565 Median :12.641 Median :12.179 Median :12.594
Mean :13.377 Mean :13.435 Mean :13.48 Mean :13.021 Mean :13.849 Mean :13.871 Mean :13.591 Mean :13.917
3rd Qu.:12.589 3rd Qu.:15.629 3rd Qu.:15.69 3rd Qu.:15.166 3rd Qu.:16.087 3rd Qu.:16.188 3rd Qu.:15.572 3rd Qu.:16.126
Max. :19.059 Max. :18.814 Max. :18.84 Max. :18.583 Max. :19.043 Max. :19.094 Max. :18.786 Max. :19.063
TM9 TM10 TM11 TM12 VBM1 VBM2 VBM3 VBM4
Min. : 6.836 Min. : 8.418 Min. : 7.003 Min. : 8.624 Min. : 1.531 Min. : 1.37 Min. : 1.336 Min. : 2.172
1st Qu.:11.502 1st Qu.:12.451 1st Qu.:11.602 1st Qu.:12.574 1st Qu.:12.655 1st Qu.:11.85 1st Qu.:11.680 1st Qu.:15.858
Median :11.502 Median :12.451 Median :11.602 Median :12.574 Median :12.655 Median :11.85 Median :11.680 Median :15.858
Mean :12.648 Mean :13.737 Mean :12.728 Mean :13.806 Mean :19.036 Mean :19.24 Mean :19.046 Mean :23.866
3rd Qu.:14.669 3rd Qu.:15.934 3rd Qu.:14.802 3rd Qu.:16.099 3rd Qu.:12.655 3rd Qu.:27.77 3rd Qu.:27.521 3rd Qu.:33.787
Max. :18.334 Max. :18.967 Max. :18.401 Max. :19.050 Max. :62.655 Max. :61.85 Max. :61.680 Max. :65.858
VBM5 VBM6 VBM7 VBM8 VBM9 VBM10 VBM11 VBM12
Min. : 1.40 Min. : 2.213 Min. : 2.978 Min. : 1.933 Min. : 1.443 Min. : 2.015 Min. : 1.554 Min. : 1.139
1st Qu.:12.00 1st Qu.:16.063 1st Qu.:19.889 1st Qu.:14.663 1st Qu.:12.216 1st Qu.:15.075 1st Qu.:12.773 1st Qu.:10.694
Median :12.00 Median :16.063 Median :19.889 Median :14.663 Median :12.216 Median :15.075 Median :12.773 Median :10.694
Mean :19.57 Mean :23.758 Mean :29.663 Mean :22.626 Mean :19.946 Mean :23.188 Mean :20.502 Mean :17.805
3rd Qu.:28.00 3rd Qu.:34.094 3rd Qu.:39.834 3rd Qu.:31.994 3rd Qu.:28.324 3rd Qu.:32.612 3rd Qu.:29.159 3rd Qu.:26.040
Max. :62.00 Max. :66.063 Max. :69.889 Max. :64.663 Max. :62.216 Max. :65.075 Max. :62.773 Max. :60.694
PDM1 PDM2 PDM3 PDM4 PDM5 PDM6 PDM7
Min. : 2.328 Min. : 1.924 Min. : 1.84 Min. : 3.929 Min. : 2.001 Min. : 4.031 Min. : 5.945
1st Qu.: 32.797 1st Qu.: 32.555 1st Qu.: 32.50 1st Qu.: 33.757 1st Qu.: 32.601 1st Qu.: 33.819 1st Qu.: 34.967
Median : 32.797 Median : 32.555 Median : 32.50 Median : 33.757 Median : 32.601 Median : 33.819 Median : 34.967
Mean : 49.116 Mean : 45.275 Mean : 45.31 Mean : 55.552 Mean : 46.111 Mean : 55.113 Mean : 69.114
3rd Qu.: 32.797 3rd Qu.: 59.244 3rd Qu.: 58.40 3rd Qu.: 79.290 3rd Qu.: 60.011 3rd Qu.: 80.313 3rd Qu.: 99.445
Max. :163.275 Max. :159.244 Max. :158.40 Max. :179.290 Max. :160.011 Max. :180.313 Max. :199.445
PDM8 PDM9 PDM10 PDM11 PDM12 RLM1 RLM2 RLM3
Min. : 3.331 Min. : 2.108 Min. : 3.537 Min. : 2.386 Min. : 1.347 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.: 33.399 1st Qu.: 32.665 1st Qu.: 33.522 1st Qu.: 32.832 1st Qu.: 32.208 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median : 33.399 Median : 32.665 Median : 33.522 Median : 32.832 Median : 32.208 Median :1.000 Median :1.000 Median :1.000
Mean : 52.821 Mean : 47.053 Mean : 54.183 Mean : 47.953 Mean : 42.323 Mean :1.374 Mean :1.423 Mean :1.425
3rd Qu.: 73.315 3rd Qu.: 61.080 3rd Qu.: 75.374 3rd Qu.: 63.862 3rd Qu.: 53.468 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :173.315 Max. :161.080 Max. :175.374 Max. :163.862 Max. :153.468 Max. :4.000 Max. :4.000 Max. :4.000
RLM4 RLM5 RLM6 RLM7 RLM8 RLM9 RLM10 RLM11
Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
Median :1.000 Median :1.000 Median :1.000 Median :1.000 Median :1.00 Median :1.000 Median :1.000 Median :1.000
Mean :1.867 Mean :1.431 Mean :1.835 Mean :1.976 Mean :1.44 Mean :1.444 Mean :1.886 Mean :1.435
3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.00 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.000
Max. :5.000 Max. :4.000 Max. :5.000 Max. :5.000 Max. :4.00 Max. :4.000 Max. :5.000 Max. :4.000
RLM12 PSEM1 PSEM2
Min. :0.000 Min. :1.500 Min. :1.500
1st Qu.:1.000 1st Qu.:1.750 1st Qu.:1.750
Median :1.000 Median :2.000 Median :2.000
Mean :1.411 Mean :1.893 Mean :1.897
3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
Max. :4.000 Max. :2.000 Max. :2.000
set.seed(1234)
ind <- sample(x=c(0,1),size=nrow(alumnos.actuales),
replace=TRUE,prob = c(0.7,0.3))
ind [1] 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0
[69] 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0
[137] 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1
[205] 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1
[273] 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0
[341] 1 0 0 1 0 0 0 0 1 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0
[409] 0 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1
[477] 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0
[545] 1 0 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
[613] 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1
[681] 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0
[749] 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
[817] 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 0 0 0 0alumnos.training <- alumnos.actuales[ind==0,]
alumnos.test <- alumnos.actuales[ind==1,]
str(alumnos.training)'data.frame': 613 obs. of 82 variables:
$ genero : Factor w/ 2 levels "1","2": 2 2 2 1 2 2 2 1 2 1 ...
$ admision.letras : num 60.1 59.1 53.1 57 61.9 ...
$ admision.numeros : num 35.2 33.2 21.3 29 38.9 ...
$ promedio.preparatoria : num 70.3 67.2 60 61 75.8 ...
$ edad.ingreso : Factor w/ 15 levels "11","12","13",..: 8 7 5 6 8 5 7 4 7 10 ...
$ evalucion.socioeconomica: Factor w/ 4 levels "1","2","3","4": 4 4 4 4 4 4 4 4 4 4 ...
$ nota.conducta : num 16 15 13 14 16 13 15 12 15 18 ...
$ BECA : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ AM1 : num 1.88 1.88 1.88 1.88 1.88 ...
$ AM2 : num 1.88 1.41 1.41 1.88 1.53 ...
$ AM3 : num 2 2 2 2 2 ...
$ AM4 : num 1.41 1.81 1.81 1.81 1.81 ...
$ AM5 : num 1.91 1.5 1.38 1.56 1.56 ...
$ AM6 : num 1.44 1.38 1.12 1.88 1.12 ...
$ AM7 : num 1.59 1.69 1.69 1.69 1.59 ...
$ AM8 : num 1.12 1.12 1.94 1.69 1.94 ...
$ AM9 : num 1.84 1.84 1.84 1.66 1.84 ...
$ AM10 : num 2 2 1.22 2 2 ...
$ AM11 : num 1.97 1.97 1.97 1.97 1.97 ...
$ AM12 : num 1.97 1.97 1.97 1.97 1.78 ...
$ CM1 : num 12 12 12 12 12 ...
$ CM2 : num 12.33 12.33 15.77 15.77 8.22 ...
$ CM3 : num 12.5 12.5 12.5 16 16 ...
$ CM4 : num 15.2 11.9 11.9 15.2 15.2 ...
$ CM5 : num 12.3 15.8 12.3 12.3 12.3 ...
$ CM6 : num 17.1 17.1 13.3 17.1 19.5 ...
$ CM7 : num 9.47 13.08 13.08 13.08 19.39 ...
$ CM8 : num 12.01 15.35 12.01 7.69 12.01 ...
$ CM9 : num 11.4 11.4 11.4 18.2 11.4 ...
$ CM10 : num 12.2 12.2 12.2 15.6 15.6 ...
$ CM11 : num 11.4 11.4 11.4 11.4 11.4 ...
$ CM12 : num 12.33 12.33 12.33 15.77 8.22 ...
$ TM1 : num 12.6 12.6 12.6 12.6 12.6 ...
$ TM2 : num 12.22 12.22 15.63 15.63 8.04 ...
$ TM3 : num 12.3 12.3 12.3 15.7 15.7 ...
$ TM4 : num 15.2 11.9 11.9 15.2 15.2 ...
$ TM5 : num 12.6 16.1 12.6 12.6 12.6 ...
$ TM6 : num 16.2 16.2 12.6 16.2 19.1 ...
$ TM7 : num 7.97 12.18 12.18 12.18 18.79 ...
$ TM8 : num 12.59 16.13 12.59 8.66 12.59 ...
$ TM9 : num 11.5 11.5 11.5 18.3 11.5 ...
$ TM10 : num 12.5 12.5 12.5 15.9 15.9 ...
$ TM11 : num 11.6 11.6 11.6 11.6 11.6 ...
$ TM12 : num 12.57 12.57 12.57 16.1 8.62 ...
$ VBM1 : num 12.7 12.7 12.7 12.7 12.7 ...
$ VBM2 : num 11.85 11.85 27.77 27.77 1.37 ...
$ VBM3 : num 11.7 11.7 11.7 27.5 27.5 ...
$ VBM4 : num 33.8 15.9 15.9 33.8 33.8 ...
$ VBM5 : num 12 28 12 12 12 ...
$ VBM6 : num 34.1 34.1 16.1 34.1 66.1 ...
$ VBM7 : num 2.98 19.89 19.89 19.89 69.89 ...
$ VBM8 : num 14.66 31.99 14.66 1.93 14.66 ...
$ VBM9 : num 12.2 12.2 12.2 62.2 12.2 ...
$ VBM10 : num 15.1 15.1 15.1 32.6 32.6 ...
$ VBM11 : num 12.8 12.8 12.8 12.8 12.8 ...
$ VBM12 : num 10.69 10.69 10.69 26.04 1.14 ...
$ PDM1 : num 32.8 32.8 32.8 32.8 32.8 ...
$ PDM2 : num 32.55 32.55 59.24 59.24 1.92 ...
$ PDM3 : num 32.5 32.5 32.5 58.4 58.4 ...
$ PDM4 : num 79.3 33.8 33.8 79.3 79.3 ...
$ PDM5 : num 32.6 60 32.6 32.6 32.6 ...
$ PDM6 : num 80.3 80.3 33.8 80.3 180.3 ...
$ PDM7 : num 5.94 34.97 34.97 34.97 199.45 ...
$ PDM8 : num 33.4 73.31 33.4 3.33 33.4 ...
$ PDM9 : num 32.7 32.7 32.7 161.1 32.7 ...
$ PDM10 : num 33.5 33.5 33.5 75.4 75.4 ...
$ PDM11 : num 32.8 32.8 32.8 32.8 32.8 ...
$ PDM12 : num 32.21 32.21 32.21 53.47 1.35 ...
$ RLM1 : num 1 1 1 1 1 4 2 1 1 1 ...
$ RLM2 : num 1 1 2 2 0 2 1 1 2 1 ...
$ RLM3 : num 1 1 1 2 2 2 2 1 2 1 ...
$ RLM4 : num 3 1 1 3 3 3 3 1 1 1 ...
$ RLM5 : num 1 2 1 1 1 2 1 1 2 1 ...
$ RLM6 : num 3 3 1 3 5 0 3 1 3 0 ...
$ RLM7 : num 0 1 1 1 5 3 1 1 3 1 ...
$ RLM8 : num 1 2 1 0 1 4 0 1 2 1 ...
$ RLM9 : num 1 1 1 4 1 2 2 1 1 2 ...
$ RLM10 : num 1 1 1 3 3 3 3 5 3 1 ...
$ RLM11 : num 1 1 1 1 1 2 1 1 1 1 ...
$ RLM12 : num 1 1 1 2 0 2 1 1 1 1 ...
$ PSEM1 : num 2 2 2 2 2 1.5 2 2 2 2 ...
$ PSEM2 : num 2 1.5 1.5 2 1.5 2 2 2 2 2 ...set.seed(2020)
wss.alumnos <-vector()
wss.alumnoslogical(0)centroides.alumnos <- 25
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)
#plot
plot(1:centroides.alumnos , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")
AnalÃsis de hombro, brazo, codo para seleccionar el centroide
imgPath.codo <- paste(local.path.imgs,"/Kmeans-codo-alumnos.png",sep = "")
img.codo.alumnos <- readPNG(imgPath.codo)
plot.new()
rasterImage(img.codo.alumnos,0,0,1,1)
set.seed(2020)
wss.alumnos <-vector()
wss.alumnoslogical(0)centroides.alumnos <- 10
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)
#plot
plot(1:centroides.alumnos , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")
imgPath.codo.seleccionado <- paste(local.path.imgs,"/Kmeans-codo-alumnos-seleccionado.png",sep = "")
img.codo.sel.alumnos <- readPNG(imgPath.codo.seleccionado)
plot.new()
rasterImage(img.codo.sel.alumnos,0,0,1,1)
clustering.kmeans <- kmeans(x=alumnos.training, centers = 4)
clustering.kmeans$withinss[1] 867289.6 2325698.5 1619780.3 3420804.7alumnos.training$genero <- as.numeric(alumnos.training$genero)
alumnos.training$edad.ingreso <- as.numeric(alumnos.training$edad.ingreso)
alumnos.training$evalucion.socioeconomica <- as.numeric(alumnos.training$evalucion.socioeconomica)
alumnos.training$BECA <- as.numeric(alumnos.training$BECA)
sum(is.na(alumnos.training))[1] 0#str(alumnos.training)datos.alumnos.df$genero <- as.numeric(datos.alumnos.df$genero)
datos.alumnos.df$edad.ingreso <- as.numeric(datos.alumnos.df$edad.ingreso)
datos.alumnos.df$BECA <- as.numeric(datos.alumnos.df$BECA)
datos.alumnos.df$evalucion.socioeconomica <- as.numeric(datos.alumnos.df$evalucion.socioeconomica)
str(datos.alumnos.df)'data.frame': 1000 obs. of 82 variables:
$ genero : num 2 2 2 1 2 2 2 2 1 2 ...
$ admision.letras : num 60.1 59.1 53.1 57 61.5 ...
$ admision.numeros : num 35.2 33.2 21.3 29 37.9 ...
$ promedio.preparatoria : num 70.3 67.2 60 61 74.4 ...
$ edad.ingreso : num 8 7 5 6 8 8 5 7 4 7 ...
$ evalucion.socioeconomica: num 4 4 4 4 4 4 4 4 4 4 ...
$ nota.conducta : num 16 15 13 14 16 16 13 15 12 15 ...
$ BECA : num 1 1 1 1 1 1 1 1 1 1 ...
$ AM1 : num 1.88 1.88 1.88 1.88 1.88 ...
$ AM2 : num 1.88 1.41 1.41 1.88 1.41 ...
$ AM3 : num 2 2 2 2 1.72 ...
$ AM4 : num 1.41 1.81 1.81 1.81 1.81 ...
$ AM5 : num 1.91 1.5 1.38 1.56 1.91 ...
$ AM6 : num 1.44 1.38 1.12 1.88 1.12 ...
$ AM7 : num 1.59 1.69 1.69 1.69 1.69 ...
$ AM8 : num 1.12 1.12 1.94 1.69 1.12 ...
$ AM9 : num 1.84 1.84 1.84 1.66 1.66 ...
$ AM10 : num 2 2 1.22 2 2 ...
$ AM11 : num 1.97 1.97 1.97 1.97 1.97 ...
$ AM12 : num 1.97 1.97 1.97 1.97 1.97 ...
$ CM1 : num 12 12 12 12 12 ...
$ CM2 : num 12.3 12.3 15.8 15.8 12.3 ...
$ CM3 : num 12.5 12.5 12.5 16 16 ...
$ CM4 : num 15.2 11.9 11.9 15.2 11.9 ...
$ CM5 : num 12.3 15.8 12.3 12.3 12.3 ...
$ CM6 : num 17.1 17.1 13.3 17.1 13.3 ...
$ CM7 : num 9.47 13.08 13.08 13.08 13.08 ...
$ CM8 : num 12.01 15.35 12.01 7.69 15.35 ...
$ CM9 : num 11.37 11.37 11.37 18.25 6.61 ...
$ CM10 : num 12.2 12.2 12.2 15.6 12.2 ...
$ CM11 : num 11.4 11.4 11.4 11.4 11.4 ...
$ CM12 : num 12.3 12.3 12.3 15.8 12.3 ...
$ TM1 : num 12.6 12.6 12.6 12.6 12.6 ...
$ TM2 : num 12.2 12.2 15.6 15.6 12.2 ...
$ TM3 : num 12.3 12.3 12.3 15.7 15.7 ...
$ TM4 : num 15.2 11.9 11.9 15.2 11.9 ...
$ TM5 : num 12.6 16.1 12.6 12.6 12.6 ...
$ TM6 : num 16.2 16.2 12.6 16.2 12.6 ...
$ TM7 : num 7.97 12.18 12.18 12.18 12.18 ...
$ TM8 : num 12.59 16.13 12.59 8.66 16.13 ...
$ TM9 : num 11.5 11.5 11.5 18.33 6.84 ...
$ TM10 : num 12.5 12.5 12.5 15.9 12.5 ...
$ TM11 : num 11.6 11.6 11.6 11.6 11.6 ...
$ TM12 : num 12.6 12.6 12.6 16.1 12.6 ...
$ VBM1 : num 12.7 12.7 12.7 12.7 12.7 ...
$ VBM2 : num 11.8 11.8 27.8 27.8 11.8 ...
$ VBM3 : num 11.7 11.7 11.7 27.5 27.5 ...
$ VBM4 : num 33.8 15.9 15.9 33.8 15.9 ...
$ VBM5 : num 12 28 12 12 12 ...
$ VBM6 : num 34.1 34.1 16.1 34.1 16.1 ...
$ VBM7 : num 2.98 19.89 19.89 19.89 19.89 ...
$ VBM8 : num 14.66 31.99 14.66 1.93 31.99 ...
$ VBM9 : num 12.22 12.22 12.22 62.22 1.44 ...
$ VBM10 : num 15.1 15.1 15.1 32.6 15.1 ...
$ VBM11 : num 12.8 12.8 12.8 12.8 12.8 ...
$ VBM12 : num 10.7 10.7 10.7 26 10.7 ...
$ PDM1 : num 32.8 32.8 32.8 32.8 32.8 ...
$ PDM2 : num 32.6 32.6 59.2 59.2 32.6 ...
$ PDM3 : num 32.5 32.5 32.5 58.4 58.4 ...
$ PDM4 : num 79.3 33.8 33.8 79.3 33.8 ...
$ PDM5 : num 32.6 60 32.6 32.6 32.6 ...
$ PDM6 : num 80.3 80.3 33.8 80.3 33.8 ...
$ PDM7 : num 5.94 34.97 34.97 34.97 34.97 ...
$ PDM8 : num 33.4 73.31 33.4 3.33 73.31 ...
$ PDM9 : num 32.66 32.66 32.66 161.08 2.11 ...
$ PDM10 : num 33.5 33.5 33.5 75.4 33.5 ...
$ PDM11 : num 32.8 32.8 32.8 32.8 32.8 ...
$ PDM12 : num 32.2 32.2 32.2 53.5 32.2 ...
$ RLM1 : num 1 1 1 1 1 1 4 2 1 1 ...
$ RLM2 : num 1 1 2 2 1 0 2 1 1 2 ...
$ RLM3 : num 1 1 1 2 2 2 2 2 1 2 ...
$ RLM4 : num 3 1 1 3 1 3 3 3 1 1 ...
$ RLM5 : num 1 2 1 1 1 1 2 1 1 2 ...
$ RLM6 : num 3 3 1 3 1 5 0 3 1 3 ...
$ RLM7 : num 0 1 1 1 1 5 3 1 1 3 ...
$ RLM8 : num 1 2 1 0 2 1 4 0 1 2 ...
$ RLM9 : num 1 1 1 4 0 1 2 2 1 1 ...
$ RLM10 : num 1 1 1 3 1 3 3 3 5 3 ...
$ RLM11 : num 1 1 1 1 1 1 2 1 1 1 ...
$ RLM12 : num 1 1 1 2 1 0 2 1 1 1 ...
$ PSEM1 : num 2 2 2 2 2 2 1.5 2 2 2 ...
$ PSEM2 : num 2 1.5 1.5 2 2 1.5 2 2 2 2 ...#ds.test <- data.frame()
#ds.test <- cbind(datos.alumnos.df$genero, datos.alumnos.df$CM1)
#str(ds.test)sum(datos.alumnos.df$AM11)[1] 1892.344colnames(datos.alumnos.df) [1] "genero" "admision.letras" "admision.numeros" "promedio.preparatoria" "edad.ingreso" "evalucion.socioeconomica" "nota.conducta" "BECA"
[9] "AM1" "AM2" "AM3" "AM4" "AM5" "AM6" "AM7" "AM8"
[17] "AM9" "AM10" "AM11" "AM12" "CM1" "CM2" "CM3" "CM4"
[25] "CM5" "CM6" "CM7" "CM8" "CM9" "CM10" "CM11" "CM12"
[33] "TM1" "TM2" "TM3" "TM4" "TM5" "TM6" "TM7" "TM8"
[41] "TM9" "TM10" "TM11" "TM12" "VBM1" "VBM2" "VBM3" "VBM4"
[49] "VBM5" "VBM6" "VBM7" "VBM8" "VBM9" "VBM10" "VBM11" "VBM12"
[57] "PDM1" "PDM2" "PDM3" "PDM4" "PDM5" "PDM6" "PDM7" "PDM8"
[65] "PDM9" "PDM10" "PDM11" "PDM12" "RLM1" "RLM2" "RLM3" "RLM4"
[73] "RLM5" "RLM6" "RLM7" "RLM8" "RLM9" "RLM10" "RLM11" "RLM12"
[81] "PSEM1" "PSEM2" #AM1 out#ds <- datos.alumnos.df
#ds.test <- data.frame()
#ds.test <- cbind(ds$genero, ds$admision.letras, ds$admision.numeros, ds$promedio.preparatoria, ds$edad.ingreso, ds$evalucion.socioeconomica, ds$nota.conducta, ds$BECA, ds$AM1 )
ds.data.analisis <- datos.alumnos.df
ds.data.analisis$AM1 <- NULL
dataframe.to.cor <- ds.data.analisis
library(corrplot)
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(dataframe.to.cor)$r
$p
$sym
AM9 AM3 AM5 VBM6 PDM6 RLM6 CM6 TM6 AM11 AM12 VBM10 PDM10 RLM10 CM10 TM10 AM7 AM8 AM4 CM1 TM1 RLM1 VBM1 PDM1 BECA CM3 TM3 PDM3 VBM3
AM9 1
AM3 1
AM5 1
VBM6 1
PDM6 1 1
RLM6 B B 1
CM6 B * B 1
TM6 B * B 1 1
AM11 1
AM12 1
VBM10 1
PDM10 B 1
RLM3 CM2 TM2 PDM2 VBM2 RLM2 CM12 TM12 PDM12 VBM12 RLM12 PSEM1 promedio.preparatoria admision.letras admision.numeros edad.ingreso
AM9
AM3
AM5
VBM6
PDM6
RLM6
CM6
TM6
AM11
AM12
VBM10
PDM10
nota.conducta genero evalucion.socioeconomica CM7 TM7 RLM7 VBM7 PDM7 CM4 TM4 RLM4 VBM4 PDM4 CM8 TM8 PDM8 VBM8 RLM8 AM6 AM10 CM9 TM9
AM9
AM3
AM5
VBM6
PDM6
RLM6
CM6
TM6
AM11
AM12
VBM10
PDM10
PDM9 VBM9 RLM9 CM5 TM5 PDM5 VBM5 RLM5 CM11 TM11 RLM11 VBM11 PDM11 AM2 PSEM2
AM9
AM3
AM5
VBM6
PDM6
RLM6
CM6
TM6
AM11
AM12
VBM10
PDM10
[ reached getOption("max.print") -- omitted 69 rows ]
attr(,"legend")
[1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1col<- colorRampPalette(c("blue", "white", "red"))(20)
# create device
?jpeg
jpeg('correlacion-alumnos-ad.jpg', width=1920, height=1080)rquery.cormat(dataframe.to.cor, type="full", col=col)$r
AM9 AM3 AM5 VBM6 PDM6 RLM6 CM6 TM6 AM11 AM12 VBM10 PDM10 RLM10 CM10 TM10 AM7
AM9 1.0000 0.150 0.17000 0.1700 0.1700 0.190 0.1600 0.1500 0.1500 0.12000 0.09400 0.0900 0.120 0.0770 0.0810 0.1100
AM3 0.1500 1.000 0.21000 0.1300 0.1300 0.160 0.1300 0.1200 0.1800 0.16000 0.15000 0.1400 0.180 0.1200 0.1300 0.2000
AM5 0.1700 0.210 1.00000 0.1200 0.1300 0.150 0.1200 0.1100 0.1300 0.18000 0.10000 0.0950 0.120 0.0840 0.0880 0.2000
VBM6 0.1700 0.130 0.12000 1.0000 1.0000 0.980 0.9600 0.9600 0.1600 0.16000 0.14000 0.1300 0.160 0.1100 0.1100 0.1800
PDM6 0.1700 0.130 0.13000 1.0000 1.0000 0.970 0.9200 0.9300 0.1600 0.16000 0.14000 0.1400 0.160 0.1100 0.1200 0.1700
RLM6 0.1900 0.160 0.15000 0.9800 0.9700 1.000 0.9800 0.9700 0.1900 0.18000 0.16000 0.1500 0.180 0.1200 0.1300 0.2100
CM6 0.1600 0.130 0.12000 0.9600 0.9200 0.980 1.0000 1.0000 0.1600 0.15000 0.13000 0.1300 0.150 0.1100 0.1100 0.1900
TM6 0.1500 0.120 0.11000 0.9600 0.9300 0.970 1.0000 1.0000 0.1500 0.14000 0.12000 0.1200 0.140 0.0980 0.1000 0.1800
AM11 0.1500 0.180 0.13000 0.1600 0.1600 0.190 0.1600 0.1500 1.0000 0.23000 0.14000 0.1300 0.160 0.1100 0.1200 0.1400
AM12 0.1200 0.160 0.18000 0.1600 0.1600 0.180 0.1500 0.1400 0.2300 1.00000 0.16000 0.1600 0.180 0.1400 0.1400 0.1500
VBM10 0.0940 0.150 0.10000 0.1400 0.1400 0.160 0.1300 0.1200 0.1400 0.16000 1.00000 0.9900 0.980 0.9500 0.9500 0.1800
PDM10 0.0900 0.140 0.09500 0.1300 0.1400 0.150 0.1300 0.1200 0.1300 0.16000 0.99000 1.0000 0.960 0.9200 0.9200 0.1700
AM8 AM4 CM1 TM1 RLM1 VBM1 PDM1 BECA CM3 TM3 PDM3 VBM3 RLM3 CM2 TM2 PDM2
AM9 0.0680 0.14000 0.1600 0.1600 0.17000 0.1800 0.18000 0.06500 0.0410 0.0380 0.0280 0.0520 0.0410 0.0490 0.0470 0.0490
AM3 0.2100 0.19000 0.1500 0.1500 0.17000 0.1700 0.17000 -0.02800 0.0110 0.0100 -0.0730 -0.0520 -0.0370 0.0860 0.0840 0.0800
AM5 0.1400 0.11000 0.1600 0.1600 0.18000 0.1900 0.18000 0.01200 0.0770 0.0740 0.0820 0.1000 0.0890 0.0730 0.0710 0.0550
VBM6 0.1300 0.13000 0.2000 0.2000 0.20000 0.2000 0.19000 -0.00790 0.0870 0.0840 0.1000 0.1200 0.1000 0.0990 0.0970 0.1100
PDM6 0.1300 0.13000 0.2000 0.2000 0.20000 0.2000 0.20000 -0.00570 0.0850 0.0820 0.1000 0.1200 0.1000 0.1000 0.1000 0.1200
RLM6 0.1400 0.16000 0.2200 0.2200 0.22000 0.2200 0.22000 -0.01500 0.1000 0.1000 0.1100 0.1400 0.1200 0.1200 0.1100 0.1300
CM6 0.1200 0.14000 0.1900 0.1900 0.19000 0.1900 0.18000 -0.01600 0.0930 0.0910 0.1000 0.1200 0.1100 0.0900 0.0890 0.1000
TM6 0.1100 0.13000 0.1800 0.1800 0.17000 0.1700 0.17000 -0.01400 0.0860 0.0840 0.0940 0.1100 0.1000 0.0810 0.0800 0.0930
AM11 0.1600 0.14000 0.1700 0.1700 0.18000 0.1900 0.19000 -0.02800 0.1100 0.1100 0.1100 0.1400 0.1200 0.1300 0.1300 0.1300
AM12 0.1400 0.13000 0.1500 0.1500 0.17000 0.1800 0.18000 -0.01300 0.1100 0.1000 0.1100 0.1400 0.1200 0.1300 0.1300 0.1100
VBM10 0.1500 0.11000 0.1700 0.1700 0.17000 0.1800 0.18000 -0.01200 0.0340 0.0320 0.0360 0.0570 0.0430 0.0720 0.0700 0.0540
PDM10 0.1400 0.10000 0.1700 0.1700 0.18000 0.1800 0.18000 -0.01200 0.0340 0.0320 0.0400 0.0590 0.0450 0.0710 0.0700 0.0500
VBM2 RLM2 CM12 TM12 PDM12 VBM12 RLM12 PSEM1 promedio.preparatoria admision.letras admision.numeros
AM9 0.0730 0.0580 0.11000 0.1200 0.09400 0.1300 0.12000 -0.2500 8.6e-03 0.00340 0.00340
AM3 0.1100 0.0950 0.06000 0.0650 0.03200 0.0830 0.06400 -0.2700 3.1e-02 0.03400 0.03400
AM5 0.0860 0.0730 0.11000 0.1100 0.08900 0.1300 0.11000 -0.2700 3.2e-02 0.03500 0.03500
VBM6 0.1300 0.1200 0.07200 0.0760 0.05600 0.0930 0.07900 -0.2400 -2.3e-02 -0.01900 -0.01900
PDM6 0.1400 0.1200 0.07700 0.0810 0.05900 0.0970 0.08300 -0.2500 -2.6e-02 -0.02100 -0.02100
RLM6 0.1500 0.1300 0.08200 0.0870 0.06300 0.1100 0.09000 -0.2900 -1.5e-02 -0.01000 -0.01000
CM6 0.1200 0.1100 0.06000 0.0630 0.04700 0.0840 0.06800 -0.2400 -1.2e-02 -0.00800 -0.00800
TM6 0.1100 0.0960 0.05300 0.0570 0.04200 0.0760 0.06100 -0.2100 -1.5e-02 -0.01100 -0.01100
AM11 0.1700 0.1500 0.07700 0.0810 0.04900 0.0930 0.07900 -0.2800 -2.5e-02 -0.02900 -0.02900
AM12 0.1500 0.1300 0.08400 0.0840 -0.04700 -0.0260 0.01300 -0.2800 -6.8e-03 -0.00044 -0.00044
VBM10 0.0780 0.0700 0.03100 0.0330 0.02500 0.0480 0.03700 -0.2300 3.1e-02 0.03500 0.03500
PDM10 0.0740 0.0670 0.02800 0.0300 0.02300 0.0450 0.03400 -0.2300 3.0e-02 0.03500 0.03500
edad.ingreso nota.conducta genero evalucion.socioeconomica CM7 TM7 RLM7 VBM7 PDM7 CM4 TM4 RLM4
AM9 -0.00170 -4.0e-03 -0.01100 -0.04700 1.1e-01 0.0950 0.1500 0.12000 0.15000 3.9e-02 0.03900 0.082
AM3 0.02700 2.7e-02 0.04600 0.03400 1.2e-01 0.1000 0.1600 0.13000 0.15000 9.4e-02 0.09400 0.140
AM5 0.03200 3.0e-02 0.01700 0.00051 1.1e-01 0.0950 0.1300 0.11000 0.13000 1.1e-01 0.11000 0.160
VBM6 -0.02400 -2.2e-02 0.00680 0.00570 3.4e-02 0.0190 0.0560 0.03100 0.04900 9.6e-02 0.09600 0.120
PDM6 -0.02700 -2.5e-02 0.00730 0.00520 4.0e-02 0.0240 0.0630 0.03700 0.05600 9.7e-02 0.09700 0.120
RLM6 -0.01600 -1.3e-02 0.01500 0.01200 4.9e-02 0.0320 0.0730 0.04600 0.06500 1.1e-01 0.11000 0.140
CM6 -0.01400 -9.4e-03 0.00870 0.00940 2.3e-02 0.0100 0.0420 0.02000 0.03600 9.5e-02 0.09500 0.120
TM6 -0.01600 -1.2e-02 0.00480 0.00670 1.4e-02 0.0024 0.0310 0.01200 0.02600 8.9e-02 0.08900 0.110
AM11 -0.02700 -2.8e-02 0.02400 0.01300 1.4e-01 0.1200 0.1700 0.14000 0.16000 8.0e-02 0.08000 0.140
AM12 -0.00710 -9.3e-03 0.03600 0.01400 1.1e-01 0.0970 0.1400 0.12000 0.14000 1.2e-01 0.12000 0.170
VBM10 0.03000 3.1e-02 -0.06300 -0.01100 5.4e-02 0.0430 0.0760 0.05700 0.07300 7.6e-02 0.07600 0.120
PDM10 0.02900 3.0e-02 -0.06100 -0.01000 5.0e-02 0.0390 0.0720 0.05300 0.06900 7.1e-02 0.07100 0.120
VBM4 PDM4 CM8 TM8 PDM8 VBM8 RLM8 AM6 AM10 CM9 TM9 PDM9 VBM9 RLM9 CM5 TM5
AM9 0.0590 0.05900 0.10000 0.1100 0.1200 0.1300 0.1200 0.1000 0.1800 -0.0240 -0.0250 -0.13000 -0.1200 -0.0940 0.043 0.0470
AM3 0.1200 0.12000 0.07600 0.0860 0.1000 0.1100 0.0940 0.1700 0.1600 0.1000 0.1000 0.10000 0.1300 0.1200 0.068 0.0730
AM5 0.1500 0.15000 0.14000 0.1400 0.1500 0.1600 0.1500 0.1600 0.1400 0.0480 0.0500 0.06200 0.0800 0.0680 0.020 0.0210
VBM6 0.1000 0.09500 0.13000 0.1400 0.1300 0.1400 0.1300 -0.0580 0.1400 0.0580 0.0600 0.06100 0.0830 0.0730 0.100 0.1100
PDM6 0.1000 0.09700 0.14000 0.1500 0.1400 0.1500 0.1400 -0.0820 0.1400 0.0560 0.0580 0.05900 0.0820 0.0710 0.110 0.1100
RLM6 0.1200 0.11000 0.14000 0.1500 0.1400 0.1600 0.1400 -0.0310 0.1600 0.0810 0.0830 0.08700 0.1100 0.1000 0.110 0.1200
CM6 0.0970 0.09200 0.11000 0.1200 0.1100 0.1200 0.1100 0.0170 0.1400 0.0690 0.0710 0.07400 0.0960 0.0850 0.092 0.0950
TM6 0.0880 0.08300 0.11000 0.1200 0.1000 0.1100 0.1000 0.0140 0.1300 0.0600 0.0610 0.06300 0.0830 0.0730 0.086 0.0890
AM11 0.1100 0.12000 0.07500 0.0850 0.0970 0.1000 0.0890 0.1500 0.1200 0.1000 0.1000 0.09800 0.1200 0.1100 0.092 0.0970
AM12 0.1500 0.16000 0.08800 0.0980 0.1100 0.1100 0.1000 0.0860 0.1700 0.0500 0.0520 0.06600 0.0880 0.0730 0.083 0.0870
VBM10 0.1200 0.12000 -0.00033 0.0084 0.0340 0.0350 0.0210 0.1400 -0.0660 0.0680 0.0700 0.09900 0.1200 0.0990 0.064 0.0680
PDM10 0.1100 0.12000 -0.00380 0.0049 0.0310 0.0320 0.0180 0.1300 -0.0840 0.0610 0.0630 0.09400 0.1100 0.0940 0.065 0.0690
PDM5 VBM5 RLM5 CM11 TM11 RLM11 VBM11 PDM11 AM2 PSEM2
AM9 0.0200 0.0510 0.0390 0.0520 0.0550 0.0760 0.0900 0.0740 0.1000 0.0370
AM3 0.0550 0.0880 0.0720 0.0780 0.0810 0.1100 0.1300 0.1100 0.1800 0.0780
AM5 -0.1200 -0.0910 -0.0610 0.0470 0.0500 0.0730 0.0900 0.0690 0.1800 0.0680
VBM6 0.0890 0.1200 0.1100 0.0380 0.0400 0.0620 0.0750 0.0630 0.1500 0.0710
PDM6 0.0940 0.1200 0.1100 0.0380 0.0400 0.0620 0.0760 0.0640 0.1600 0.0710
RLM6 0.0980 0.1300 0.1200 0.0550 0.0580 0.0810 0.0960 0.0800 0.1800 0.0860
CM6 0.0750 0.1000 0.0920 0.0450 0.0470 0.0650 0.0770 0.0640 0.1400 0.0740
TM6 0.0680 0.0960 0.0850 0.0380 0.0400 0.0570 0.0680 0.0560 0.1300 0.0680
AM11 0.0940 0.1300 0.1100 0.0590 0.0590 0.0015 -0.0180 -0.0390 0.1700 0.0760
AM12 0.0890 0.1100 0.0960 0.0610 0.0640 0.0880 0.1100 0.0840 0.1200 0.0760
VBM10 0.0420 0.0700 0.0600 0.0200 0.0220 0.0260 0.0340 0.0150 0.1300 0.0630
PDM10 0.0420 0.0690 0.0600 0.0180 0.0210 0.0240 0.0320 0.0120 0.1200 0.0610
[ reached getOption("max.print") -- omitted 69 rows ]
$p
AM9 AM3 AM5 VBM6 PDM6 RLM6 CM6 TM6 AM11 AM12 VBM10 PDM10 RLM10 CM10 TM10 AM7
AM9 0.0e+00 1.3e-06 1.0e-07 6.5e-08 3.9e-08 1.6e-09 2.5e-07 1.4e-06 2.6e-06 2.1e-04 2.8e-03 4.6e-03 1.6e-04 1.5e-02 1.0e-02 4.5e-04
AM3 1.3e-06 0.0e+00 1.1e-11 2.6e-05 2.1e-05 2.7e-07 2.0e-05 1.3e-04 1.7e-08 8.2e-07 2.0e-06 5.3e-06 6.8e-09 7.9e-05 3.8e-05 1.2e-10
AM5 1.0e-07 1.1e-11 0.0e+00 8.6e-05 5.9e-05 3.0e-06 1.4e-04 6.0e-04 2.8e-05 6.6e-09 1.6e-03 2.7e-03 8.4e-05 7.6e-03 5.3e-03 4.8e-10
VBM6 6.5e-08 2.6e-05 8.6e-05 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 6.7e-07 8.2e-07 1.5e-05 2.0e-05 7.6e-07 5.1e-04 3.3e-04 1.5e-08
PDM6 3.9e-08 2.1e-05 5.9e-05 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 6.7e-07 4.4e-07 9.6e-06 1.3e-05 4.5e-07 4.1e-04 2.6e-04 2.8e-08
RLM6 1.6e-09 2.7e-07 3.0e-06 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 3.5e-09 1.1e-08 7.6e-07 1.1e-06 1.6e-08 7.5e-05 4.2e-05 3.9e-11
CM6 2.5e-07 2.0e-05 1.4e-04 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 3.3e-07 2.7e-06 3.5e-05 4.9e-05 2.3e-06 8.0e-04 5.3e-04 1.7e-09
TM6 1.4e-06 1.3e-04 6.0e-04 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 2.9e-06 1.8e-05 1.3e-04 1.8e-04 1.3e-05 1.9e-03 1.3e-03 1.8e-08
AM11 2.6e-06 1.7e-08 2.8e-05 6.7e-07 6.7e-07 3.5e-09 3.3e-07 2.9e-06 0.0e+00 2.4e-13 1.8e-05 3.4e-05 2.6e-07 4.4e-04 2.5e-04 4.6e-06
AM12 2.1e-04 8.2e-07 6.6e-09 8.2e-07 4.4e-07 1.1e-08 2.7e-06 1.8e-05 2.4e-13 0.0e+00 3.3e-07 5.4e-07 1.1e-08 1.7e-05 9.8e-06 1.6e-06
VBM10 2.8e-03 2.0e-06 1.6e-03 1.5e-05 9.6e-06 7.6e-07 3.5e-05 1.3e-04 1.8e-05 3.3e-07 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 2.2e-08
PDM10 4.6e-03 5.3e-06 2.7e-03 2.0e-05 1.3e-05 1.1e-06 4.9e-05 1.8e-04 3.4e-05 5.4e-07 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 4.7e-08
AM8 AM4 CM1 TM1 RLM1 VBM1 PDM1 BECA CM3 TM3 PDM3 VBM3 RLM3 CM2
AM9 3.2e-02 9.8e-06 1.8e-07 1.7e-07 2.6e-08 1.7e-08 1.4e-08 3.9e-02 2.000000e-01 2.3e-01 3.800000e-01 9.8e-02 1.9e-01 1.2e-01
AM3 1.3e-11 2.2e-09 4.1e-06 3.2e-06 1.3e-07 4.7e-08 4.1e-08 3.8e-01 7.300000e-01 7.5e-01 2.100000e-02 1.0e-01 2.5e-01 6.6e-03
AM5 1.7e-05 3.2e-04 3.5e-07 2.2e-07 1.1e-08 3.3e-09 4.1e-09 7.1e-01 1.500000e-02 1.9e-02 9.900000e-03 1.0e-03 5.0e-03 2.1e-02
VBM6 4.8e-05 3.4e-05 2.1e-10 1.7e-10 2.9e-10 3.4e-10 5.3e-10 8.0e-01 6.200000e-03 8.0e-03 1.500000e-03 1.1e-04 9.3e-04 1.8e-03
PDM6 3.2e-05 3.8e-05 1.2e-10 9.7e-11 1.3e-10 1.4e-10 2.1e-10 8.6e-01 7.200000e-03 9.3e-03 1.400000e-03 1.1e-04 9.8e-04 1.1e-03
RLM6 6.7e-06 3.3e-07 1.0e-12 6.9e-13 7.5e-13 6.9e-13 1.3e-12 6.4e-01 1.100000e-03 1.6e-03 4.000000e-04 1.1e-05 1.5e-04 2.5e-04
CM6 1.4e-04 1.1e-05 9.0e-10 6.8e-10 1.8e-09 2.2e-09 3.8e-09 6.2e-01 3.200000e-03 4.1e-03 1.600000e-03 1.1e-04 7.1e-04 4.4e-03
TM6 3.5e-04 7.0e-05 9.9e-09 8.0e-09 2.6e-08 3.5e-08 5.7e-08 6.7e-01 6.300000e-03 7.9e-03 2.800000e-03 2.8e-04 1.5e-03 1.1e-02
AM11 2.3e-07 4.3e-06 1.0e-07 6.7e-08 4.3e-09 1.4e-09 1.8e-09 3.7e-01 3.800000e-04 6.0e-04 8.400000e-04 9.3e-06 1.2e-04 2.6e-05
AM12 1.2e-05 6.3e-05 1.1e-06 8.8e-07 3.3e-08 1.2e-08 1.0e-08 6.8e-01 7.900000e-04 1.2e-03 6.700000e-04 1.0e-05 1.5e-04 3.8e-05
VBM10 3.2e-06 6.0e-04 1.5e-07 1.2e-07 3.2e-08 2.1e-08 2.4e-08 7.1e-01 2.800000e-01 3.2e-01 2.500000e-01 7.1e-02 1.8e-01 2.3e-02
PDM10 8.8e-06 1.0e-03 9.3e-08 7.4e-08 2.5e-08 1.8e-08 2.1e-08 7.0e-01 2.800000e-01 3.2e-01 2.100000e-01 6.2e-02 1.6e-01 2.5e-02
TM2 PDM2 VBM2 RLM2 CM12 TM12 PDM12 VBM12 RLM12 PSEM1 promedio.preparatoria admision.letras
AM9 1.4e-01 1.2e-01 2.1e-02 6.9e-02 3.6e-04 2.3e-04 2.9e-03 1.9e-05 1.4e-04 1.3e-15 0.790 0.9100
AM3 8.1e-03 1.2e-02 2.9e-04 2.6e-03 5.9e-02 4.0e-02 3.1e-01 8.7e-03 4.3e-02 7.4e-18 0.330 0.2900
AM5 2.5e-02 8.5e-02 6.7e-03 2.1e-02 6.7e-04 4.4e-04 4.8e-03 5.2e-05 3.0e-04 6.5e-18 0.300 0.2700
VBM6 2.1e-03 4.0e-04 3.6e-05 2.6e-04 2.3e-02 1.7e-02 7.8e-02 3.2e-03 1.3e-02 5.5e-15 0.460 0.5600
PDM6 1.3e-03 2.3e-04 1.8e-05 1.4e-04 1.5e-02 1.1e-02 6.2e-02 2.1e-03 8.7e-03 1.3e-15 0.410 0.5100
RLM6 3.0e-04 6.7e-05 2.2e-06 2.7e-05 9.3e-03 6.2e-03 4.5e-02 7.2e-04 4.5e-03 1.8e-20 0.630 0.7500
CM6 4.9e-03 1.4e-03 1.6e-04 8.7e-04 5.9e-02 4.5e-02 1.3e-01 8.0e-03 3.1e-02 4.7e-14 0.700 0.8000
TM6 1.2e-02 3.3e-03 5.7e-04 2.5e-03 9.3e-02 7.4e-02 1.8e-01 1.6e-02 5.3e-02 9.5e-12 0.640 0.7300
AM11 3.5e-05 3.8e-05 1.4e-07 3.8e-06 1.5e-02 1.0e-02 1.2e-01 3.2e-03 1.2e-02 1.2e-19 0.440 0.3600
AM12 4.9e-05 3.5e-04 3.2e-06 2.4e-05 8.1e-03 8.2e-03 1.3e-01 4.1e-01 6.8e-01 3.2e-19 0.830 0.9900
VBM10 2.6e-02 8.6e-02 1.4e-02 2.7e-02 3.3e-01 3.0e-01 4.2e-01 1.3e-01 2.4e-01 9.4e-14 0.330 0.2600
PDM10 2.8e-02 1.1e-01 1.9e-02 3.4e-02 3.8e-01 3.4e-01 4.8e-01 1.6e-01 2.8e-01 2.1e-13 0.340 0.2800
admision.numeros edad.ingreso nota.conducta genero evalucion.socioeconomica CM7 TM7 RLM7 VBM7 PDM7 CM4
AM9 0.9100 0.9600 0.9000 7.3e-01 1.4e-01 4.5e-04 2.8e-03 2.8e-06 1.2e-04 3.1e-06 2.2e-01
AM3 0.2900 0.3900 0.4000 1.5e-01 2.8e-01 1.7e-04 1.6e-03 5.8e-07 6.6e-05 1.1e-06 2.9e-03
AM5 0.2700 0.3100 0.3400 6.0e-01 9.9e-01 6.2e-04 2.6e-03 2.8e-05 4.6e-04 5.2e-05 4.3e-04
VBM6 0.5600 0.4400 0.4900 8.3e-01 8.6e-01 2.9e-01 5.5e-01 7.9e-02 3.3e-01 1.2e-01 2.3e-03
PDM6 0.5100 0.4000 0.4300 8.2e-01 8.7e-01 2.1e-01 4.4e-01 4.7e-02 2.4e-01 7.8e-02 2.2e-03
RLM6 0.7500 0.6100 0.6700 6.3e-01 7.0e-01 1.2e-01 3.1e-01 2.1e-02 1.5e-01 3.9e-02 5.5e-04
CM6 0.8000 0.6700 0.7700 7.8e-01 7.7e-01 4.7e-01 7.5e-01 1.9e-01 5.2e-01 2.6e-01 2.6e-03
TM6 0.7300 0.6100 0.7100 8.8e-01 8.3e-01 6.6e-01 9.4e-01 3.2e-01 7.2e-01 4.2e-01 5.0e-03
AM11 0.3600 0.3900 0.3700 4.5e-01 6.9e-01 8.5e-06 9.9e-05 5.6e-08 6.7e-06 1.9e-07 1.1e-02
AM12 0.9900 0.8200 0.7700 2.6e-01 6.6e-01 3.9e-04 2.1e-03 5.7e-06 1.6e-04 8.2e-06 8.4e-05
VBM10 0.2600 0.3400 0.3300 4.8e-02 7.2e-01 8.7e-02 1.8e-01 1.6e-02 7.1e-02 2.1e-02 1.7e-02
PDM10 0.2800 0.3500 0.3500 5.5e-02 7.5e-01 1.1e-01 2.2e-01 2.3e-02 9.5e-02 2.9e-02 2.5e-02
TM4 RLM4 VBM4 PDM4 CM8 TM8 PDM8 VBM8 RLM8 AM6 AM10 CM9 TM9 PDM9 VBM9 RLM9
AM9 2.2e-01 9.7e-03 6.3e-02 6.1e-02 9.6e-04 3.9e-04 1.0e-04 5.4e-05 2.2e-04 1.4e-03 1.2e-08 4.5e-01 4.4e-01 2.0e-05 1.2e-04 2.9e-03
AM3 3.0e-03 9.9e-06 1.7e-04 1.5e-04 1.6e-02 6.8e-03 1.0e-03 6.4e-04 3.0e-03 9.3e-08 2.6e-07 1.4e-03 1.1e-03 1.3e-03 3.6e-05 1.8e-04
AM5 4.4e-04 3.1e-07 3.3e-06 1.6e-06 1.8e-05 4.8e-06 9.1e-07 3.2e-07 2.5e-06 5.2e-07 1.1e-05 1.3e-01 1.1e-01 4.9e-02 1.2e-02 3.2e-02
VBM6 2.4e-03 1.6e-04 1.6e-03 2.6e-03 3.2e-05 7.8e-06 4.2e-05 8.0e-06 3.8e-05 6.6e-02 1.1e-05 6.8e-02 6.0e-02 5.4e-02 8.4e-03 2.2e-02
PDM6 2.2e-03 1.2e-04 1.3e-03 2.1e-03 1.4e-05 3.3e-06 1.6e-05 2.8e-06 1.5e-05 9.8e-03 1.2e-05 7.7e-02 6.8e-02 6.3e-02 9.7e-03 2.5e-02
RLM6 5.7e-04 1.1e-05 2.2e-04 3.7e-04 7.3e-06 1.2e-06 4.2e-06 6.2e-07 5.2e-06 3.4e-01 4.0e-07 1.0e-02 8.3e-03 5.8e-03 3.5e-04 1.5e-03
CM6 2.7e-03 2.1e-04 2.2e-03 3.7e-03 3.1e-04 8.5e-05 4.2e-04 9.7e-05 4.0e-04 6.0e-01 8.2e-06 2.8e-02 2.4e-02 1.9e-02 2.5e-03 7.2e-03
TM6 5.1e-03 6.7e-04 5.3e-03 8.8e-03 7.3e-04 2.4e-04 1.4e-03 3.5e-04 1.1e-03 6.7e-01 3.3e-05 6.0e-02 5.3e-02 4.6e-02 8.7e-03 2.0e-02
AM11 1.1e-02 8.0e-06 3.4e-04 2.6e-04 1.7e-02 6.8e-03 2.1e-03 1.1e-03 5.0e-03 1.6e-06 8.0e-05 1.2e-03 9.6e-04 2.0e-03 9.0e-05 2.9e-04
AM12 8.8e-05 3.3e-08 9.7e-07 6.9e-07 5.5e-03 1.9e-03 6.3e-04 2.9e-04 1.5e-03 6.7e-03 9.7e-08 1.2e-01 1.0e-01 3.6e-02 5.4e-03 2.0e-02
VBM10 1.7e-02 7.9e-05 2.5e-04 9.9e-05 9.9e-01 7.9e-01 2.8e-01 2.7e-01 5.1e-01 1.8e-05 3.7e-02 3.2e-02 2.8e-02 1.7e-03 2.5e-04 1.7e-03
PDM10 2.5e-02 1.4e-04 4.3e-04 1.7e-04 9.0e-01 8.8e-01 3.2e-01 3.1e-01 5.8e-01 4.3e-05 7.7e-03 5.2e-02 4.6e-02 2.8e-03 4.8e-04 3.1e-03
CM5 TM5 PDM5 VBM5 RLM5 CM11 TM11 RLM11 VBM11 PDM11 AM2 PSEM2
AM9 1.8e-01 1.4e-01 5.3e-01 1.1e-01 2.2e-01 9.8e-02 8.3e-02 1.6e-02 4.2e-03 2.0e-02 1.3e-03 2.4e-01
AM3 3.1e-02 2.1e-02 8.1e-02 5.3e-03 2.3e-02 1.4e-02 1.1e-02 5.5e-04 5.4e-05 6.2e-04 1.0e-08 1.3e-02
AM5 5.2e-01 5.0e-01 1.2e-04 3.9e-03 5.2e-02 1.4e-01 1.1e-01 2.1e-02 4.2e-03 3.0e-02 1.3e-08 3.0e-02
VBM6 9.0e-04 5.7e-04 4.9e-03 1.6e-04 7.4e-04 2.3e-01 2.0e-01 5.1e-02 1.8e-02 4.6e-02 1.3e-06 2.5e-02
PDM6 5.3e-04 3.3e-04 2.9e-03 7.4e-05 4.0e-04 2.3e-01 2.1e-01 4.9e-02 1.6e-02 4.2e-02 5.8e-07 2.5e-02
RLM6 3.2e-04 1.8e-04 2.0e-03 3.2e-05 2.3e-04 8.2e-02 6.9e-02 1.1e-02 2.5e-03 1.1e-02 2.2e-08 6.6e-03
CM6 3.7e-03 2.5e-03 1.8e-02 1.1e-03 3.7e-03 1.5e-01 1.4e-01 3.9e-02 1.5e-02 4.2e-02 6.5e-06 1.9e-02
TM6 6.7e-03 4.7e-03 3.0e-02 2.5e-03 7.1e-03 2.3e-01 2.1e-01 7.4e-02 3.3e-02 7.4e-02 4.1e-05 3.2e-02
AM11 3.5e-03 2.2e-03 3.0e-03 7.2e-05 8.6e-04 6.3e-02 6.2e-02 9.6e-01 5.6e-01 2.2e-01 2.8e-08 1.6e-02
AM12 8.6e-03 6.1e-03 5.1e-03 3.8e-04 2.4e-03 5.5e-02 4.2e-02 5.2e-03 7.6e-04 8.1e-03 7.9e-05 1.7e-02
VBM10 4.2e-02 3.2e-02 1.9e-01 2.8e-02 5.8e-02 5.3e-01 4.8e-01 4.1e-01 2.8e-01 6.3e-01 6.6e-05 4.5e-02
PDM10 3.9e-02 2.9e-02 1.8e-01 2.8e-02 5.6e-02 5.6e-01 5.1e-01 4.6e-01 3.1e-01 7.0e-01 8.2e-05 5.4e-02
[ reached getOption("max.print") -- omitted 69 rows ]
$sym
AM9 AM3 AM5 VBM6 PDM6 RLM6 CM6 TM6 AM11 AM12 VBM10 PDM10 RLM10 CM10 TM10 AM7 AM8 AM4 CM1 TM1 RLM1 VBM1 PDM1 BECA CM3 TM3 PDM3 VBM3
AM9 1
AM3 1
AM5 1
VBM6 1
PDM6 1 1
RLM6 B B 1
CM6 B * B 1
TM6 B * B 1 1
AM11 1
AM12 1
VBM10 1
PDM10 B 1
RLM3 CM2 TM2 PDM2 VBM2 RLM2 CM12 TM12 PDM12 VBM12 RLM12 PSEM1 promedio.preparatoria admision.letras admision.numeros edad.ingreso
AM9
AM3
AM5
VBM6
PDM6
RLM6
CM6
TM6
AM11
AM12
VBM10
PDM10
nota.conducta genero evalucion.socioeconomica CM7 TM7 RLM7 VBM7 PDM7 CM4 TM4 RLM4 VBM4 PDM4 CM8 TM8 PDM8 VBM8 RLM8 AM6 AM10 CM9 TM9
AM9
AM3
AM5
VBM6
PDM6
RLM6
CM6
TM6
AM11
AM12
VBM10
PDM10
PDM9 VBM9 RLM9 CM5 TM5 PDM5 VBM5 RLM5 CM11 TM11 RLM11 VBM11 PDM11 AM2 PSEM2
AM9
AM3
AM5
VBM6
PDM6
RLM6
CM6
TM6
AM11
AM12
VBM10
PDM10
[ reached getOption("max.print") -- omitted 69 rows ]
attr(,"legend")
[1] 0 ‘ ’ 0.3 ‘.’ 0.6 ‘,’ 0.8 ‘+’ 0.9 ‘*’ 0.95 ‘B’ 1dev.off()quartz_off_screen
2 
cormat<-rquery.cormat(dataframe.to.cor, type="full", col=col)
NA
NAplot.relaciones <- function(x="", y=""){
alumnos.training$cluster <- clustering.kmeans$cluster
#plot(alumnos.training)
plot(alumnos.training[,c(x, y) ],
col = clustering.kmeans$cluster)
}plot.relaciones("BECA", "evalucion.socioeconomica")
plot.relaciones("PDM1", "PSEM1")
plot.relaciones("VBM1", "PSEM1")
plot(alumnos.training[,c("VBM1", "PSEM1") ],
col = 1:3)
---
title: "R Proyecto Final"
output: html_notebook
---
```{r}
#Modificar variable para especificar directorio del Proyecto Final
#local.path <- "/Users/akcasill/Downloads"

user.path <- "/Users/jos/Downloads/"

local.path <- paste(user.path ,"mcc-ad/data",sep = "")
local.path.imgs <- paste(user.path ,"mcc-ad/imgs",sep = "")
```


```{r}
#Dependencies
#install.packages("png")
library(png)
```

#ASISTENCIAS TOTALES
```{r}
setwd(local.path)
# son 9 semestres de 6 materias cada uno. 
# 1.- Asistencias Totales
load("AsistenciasTotales.R")
class(asistencias.totales)
length(asistencias.totales)
class(asistencias.totales[[1]])
dim(asistencias.totales[[1]])
class(asistencias.totales[1])

asistencias.totales[[1]][1:10,1:10]
```
```{r}
#Asistencias
#===================
#Definición Valores
#===================
# 2 El alumno tiene asistnecia completa.
# 1 El alumno tiene retardo.
# 0 El alumno tiene falta.

#Sólo tomar las primeras 12 materias (Columnas)
for(i in 1:length(asistencias.totales)){
  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
}
asistencias.totales[[4]]
```
Tamaño de Lista de Asistencia de Alumnos:
```{r}
length(asistencias.totales)
```
Lista de total Asistencias por Alumno
```{r}
#
#for(i in 1:length(asistencias.totales)){
#  asistencias.totales[[i]] <- asistencias.totales[[i]][,1:12]
#}
asistencias.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(asistencias.totales)){
  for(j in 1:12){
    asistencias.alumnos[i,j] <- sum(asistencias.totales[[i]][,j])/32    
  }
}

asistencias.alumnos[1,]
asistencias.df <- as.data.frame(asistencias.alumnos)

#Asistencia Materias Ejemplo: AM1 = Asistencia Materia 1
colnames(asistencias.df) <- c('AM1','AM2','AM3','AM4','AM5','AM6','AM7','AM8','AM9','AM10','AM11','AM12')
#DATA FRAME DE ASISTENCIAS ALUMNOS
#=================================
#Suma de asistencias por Materia
#=================================
asistencias.df
```
#PERFIL ALUMNOS
```{r}
setwd(local.path)
print("Summary")
load("perfilAlumnos.R")
#head(perfil.alumnos,1)
str(perfil.alumnos)
summary(perfil.alumnos)

#===================
#Definición Valores
#===================
# Genero: 2 Hombre, 1 Mujer.
# admision.letras: Calificación Examen Admisión Español
# admision.numeros: Calificación Examen Admisión Matemáticas
# promedio.preparatoria: Calificación Promedio Preparatoria   
# edad.ingreso: Edad, variable numérica             
# evalucion.socioeconomica: 1 más privilegiado, 4 menos privilagiado
# nota.conducta: Calificación subjetiva. 

```

```{r}
perfil.alumnos$genero <- factor(perfil.alumnos$genero)
perfil.alumnos$evalucion.socioeconomica <-
  factor(perfil.alumnos$evalucion.socioeconomica)

perfil.alumnos$edad.ingreso <- 
  factor(perfil.alumnos$edad.ingreso)
```


#DATAFRAME CALIFICACIONES ALUMNOS
```{r}
setwd(local.path)
print("Summary")
# 3 1000 matrices de 2 x 54, calificación entre 1 y 20
load("ResultadosExamenes.R")
#resultados.examenes.totales

examenes.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.examenes.totales)){
  for(j in 1:12){
    examenes.alumnos[i,j] <- sum(resultados.examenes.totales[[i]][,j])/2    
  }
}

examenes.alumnos[1,]
cal.alumnos.df <- as.data.frame(examenes.alumnos)
#Calificaciones Materias Ejemplo: CM2 = Calificiación Promedio Materia 2
colnames(cal.alumnos.df) <- c('CM1','CM2','CM3','CM4','CM5','CM6','CM7','CM8','CM9','CM10','CM11','CM12')
#===================
#Definición Valores
#===================
# CM1: Calificación Materia 1 valor Máximo 20
cal.alumnos.df
```

#TRABAJOS POR CLASE
```{r}
setwd(local.path)
print("Summary")
# 4 1000 matrices de 4 x 54, son 4 trabajos por clase, entre 1 y 20
load("ResultadoTrabajos.R")
resultados.trabajos.totales[[2]][,1]

tareas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:length(resultados.trabajos.totales)){
  for(j in 1:12){
    tareas.alumnos[i,j] <- sum(resultados.trabajos.totales[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
tareas.alumnos.df <- as.data.frame(tareas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(tareas.alumnos.df) <- c('TM1','TM2','TM3','TM4','TM5','TM6','TM7','TM8','TM9','TM10','TM11','TM12')
#===================
#Definición Valores
#===================
# TM1: Calificación Tarea Materia 1 valor Máximo 20
tareas.alumnos.df
```

#VISITAS BIBLIOTECA
```{r}
# 5 Redondear. Uso físico y virtual. vector. 1000 Matrices, número de veces que asistio a la biblioteca por materia
setwd(local.path)
load("UsoBiblioteca.R")
length(uso.biblioteca.totales)
mi.val <- uso.biblioteca.totales[[1]][1,1]
mi.val
mi.val <- as.data.frame(mi.val)
mi.val

visitas.biblio.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    visitas.biblio.alumnos[i,j] <- uso.biblioteca.totales[[i]][1,j]
  }
}

visitas.biblio.alumnos.df <- as.data.frame(visitas.biblio.alumnos)
#Visitas Biblioteca  Ejemplo: VBM2 = Visitas Biblioteca  Materia 2
colnames(visitas.biblio.alumnos.df) <- c('VBM1','VBM2','VBM3','VBM4','VBM5','VBM6','VBM7','VBM8','VBM9','VBM10','VBM11','VBM12')
#===================
#Definición Valores
#===================
# VBM1: Visitas Biblioteca Materia 1
visitas.biblio.alumnos.df


```

#USO DE PLATAFORMAS DIGITALES
```{r}
# 6 Redondear, vector. Uso de Canvas o de Plataforma digital.
setwd(local.path)
load("UsoPlataforma.R")
#uso.plataforma.totales
uso.plataforma.totales[[1]][,1:12]
plataformas.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    plataformas.alumnos[i,j] <- uso.plataforma.totales[[i]][1,j]
  }
}

#tareas.alumnos[1,]
plataformas.alumnos.df <- as.data.frame(plataformas.alumnos)
#Tareas Materias Ejemplo: TM2 = Tareas Promedio Materia 2
colnames(plataformas.alumnos.df) <- c('PDM1','PDM2','PDM3','PDM4','PDM5','PDM6','PDM7','PDM8','PDM9','PDM10','PDM11','PDM12')
#===================
#Definición Valores
#===================
# PDM1: Plataformas Digitales Materia 1 valor Máximo 20
plataformas.alumnos.df

```

#APARTADO DE LIBROS POR MATERIA
```{r}
# 7
setwd(local.path)
load("ApartadoDeLibros.R") #1000 matrices, cantidad de libros que el alumno reservó por materia.
separacion.libros.totales[[1]][,1:12]

reserva.libros.alumnos <- matrix(1:12000, nrow=1000, ncol=12)

for(i in 1:1000){
  for(j in 1:12){
    reserva.libros.alumnos[i,j] <- separacion.libros.totales[[i]][1,j]
  }
}

reserva.libros.alumnos.df <- as.data.frame(reserva.libros.alumnos)
#Reserva de Libris Ejemplo: RLM2 = Reserva de Libros Por Materia 2
colnames(reserva.libros.alumnos.df) <- c('RLM1','RLM2','RLM3','RLM4','RLM5','RLM6','RLM7','RLM8','RLM9','RLM10','RLM11','RLM12')
#===================
#Definición Valores
#===================
# RLM1: Reserva de Libros pro Materia 1
reserva.libros.alumnos.df

```

#DISTRIBUCIÓN DE BECAS ALUMNOS
```{r}
# 8 vector binario, 1 tiene beca, 0 no tiene Beca
setwd(local.path)
load("Becas.R")
distribucion.becas[[1]]
sum(distribucion.becas)

becas.alumnos <- matrix(1:1000, nrow=1000, ncol=1)

for(i in 1:1000){
    becas.alumnos[i] <- distribucion.becas[i]
}

becas.alumnos.df <- as.data.frame(becas.alumnos)
colnames(becas.alumnos.df) <- c('BECA')
#===================
#Definición Valores
#===================
# BECA: Tiene Beca 1
becas.alumnos.df
#Necesita ser un factor por que solo tiene dos valores 0 o 1 
becas.alumnos.df$BECA <- as.factor(becas.alumnos.df$BECA)
becas.alumnos.df
```

#HISTORIAL DE PAGOS ALUMNOS
```{r}
# 9  2 en tiempo, 1 retraso, 0, Son 9 semestres pero hay que user sólo 2 primeras columnas, 4 pagos.
setwd(local.path)
load("HistorialPagos.R")
length(registro.pagos)
registro.pagos[[500]]


pagos.alumnos <- matrix(1:2000, nrow=1000, ncol=2)

for(i in 1:1000){
  for(j in 1:2){
    pagos.alumnos[i,j] <- sum(registro.pagos[[i]][,j])/4    
  }
}

#tareas.alumnos[1,]
pagos.alumnos.df <- as.data.frame(pagos.alumnos)
#Pago Semestre: PSEM2 = Pago Semestre 2
colnames(pagos.alumnos.df) <- c('PSEM1','PSEM2')
#===================
#Definición Valores
#===================
# PSEM1: Suma de pagos semestre 1, 2 valor máximo.
pagos.alumnos.df

```



```{r}
datos.alumnos.df <- cbind.data.frame(perfil.alumnos,
                                     becas.alumnos.df,
                              asistencias.df,
                              cal.alumnos.df,
                              tareas.alumnos.df,
                              visitas.biblio.alumnos.df,
                              plataformas.alumnos.df,
                              reserva.libros.alumnos.df,
                              pagos.alumnos.df)
datos.alumnos.df

```

```{r}
str(datos.alumnos.df)
```

```{r}
summary(datos.alumnos.df)
datos.integrados <- datos.alumnos.df
```

```{r}
setwd(local.path)
save(datos.integrados, file="datos.integrados.R")
getwd()
load("datos.integrados.R")
datos.integrados
head(datos.integrados)

```

#Separar 100 alumnos que no entraran en Kmeans
```{r}
set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(datos.integrados),
              replace=TRUE,prob = c(0.9,0.1))
ind

alumnos.nuevos <- datos.integrados[ind==1,]
alumnos.actuales <- datos.integrados[ind==0,]

alumnos.nuevos
alumnos.actuales
```


```{r}
summary(alumnos.nuevos)
```

```{r}
summary(alumnos.actuales)
```


```{r}
set.seed(1234)

ind <- sample(x=c(0,1),size=nrow(alumnos.actuales),
              replace=TRUE,prob = c(0.7,0.3))
ind

alumnos.training <- alumnos.actuales[ind==0,]
alumnos.test <- alumnos.actuales[ind==1,]

str(alumnos.training)
```

```{r}
set.seed(2020)
wss.alumnos <-vector()
wss.alumnos
centroides.alumnos <- 25
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")
```



### Analísis de hombro, brazo, codo para seleccionar el centroide
```{r}
imgPath.codo <- paste(local.path.imgs,"/Kmeans-codo-alumnos.png",sep = "")


img.codo.alumnos <- readPNG(imgPath.codo)
plot.new()
rasterImage(img.codo.alumnos,0,0,1,1)
```

```{r}
set.seed(2020)
wss.alumnos <-vector()
wss.alumnos
centroides.alumnos <- 10
for ( i in 1:centroides.alumnos ) wss.alumnos[i] <- sum(kmeans(alumnos.training,centers = i)$withinss)

#plot
plot(1:centroides.alumnos  , wss.alumnos , type="b", xlab="Numer de clusters", ylab="Error standard")
```

```{r}
imgPath.codo.seleccionado <- paste(local.path.imgs,"/Kmeans-codo-alumnos-seleccionado.png",sep = "")


img.codo.sel.alumnos <- readPNG(imgPath.codo.seleccionado)
plot.new()
rasterImage(img.codo.sel.alumnos,0,0,1,1)

```


```{r}

centroides.alumnos <- 4
clustering.kmeans <- kmeans(x=alumnos.training, centers = centroides.alumnos)
clustering.kmeans$withinss
```

```{r}
alumnos.training$genero <- as.numeric(alumnos.training$genero)
alumnos.training$edad.ingreso <- as.numeric(alumnos.training$edad.ingreso)
alumnos.training$evalucion.socioeconomica <- as.numeric(alumnos.training$evalucion.socioeconomica)
alumnos.training$BECA <- as.numeric(alumnos.training$BECA)
sum(is.na(alumnos.training))
#str(alumnos.training)
```

```{r}
datos.alumnos.df$genero <- as.numeric(datos.alumnos.df$genero)
datos.alumnos.df$edad.ingreso <- as.numeric(datos.alumnos.df$edad.ingreso)
datos.alumnos.df$BECA <- as.numeric(datos.alumnos.df$BECA)
datos.alumnos.df$evalucion.socioeconomica <- as.numeric(datos.alumnos.df$evalucion.socioeconomica)
str(datos.alumnos.df)

#ds.test <- data.frame()
#ds.test <- cbind(datos.alumnos.df$genero, datos.alumnos.df$CM1)
#str(ds.test)
```

```{r}
sum(datos.alumnos.df$AM11)
colnames(datos.alumnos.df)
#AM1 out
```



```{r}
#ds <- datos.alumnos.df
#ds.test <- data.frame()
#ds.test <- cbind(ds$genero, ds$admision.letras, ds$admision.numeros, ds$promedio.preparatoria, ds$edad.ingreso, ds$evalucion.socioeconomica, ds$nota.conducta, ds$BECA, ds$AM1 )
ds.data.analisis <- datos.alumnos.df
ds.data.analisis$AM1 <- NULL


dataframe.to.cor <- ds.data.analisis

library(corrplot)
source("http://www.sthda.com/upload/rquery_cormat.r")
rquery.cormat(dataframe.to.cor)

col<- colorRampPalette(c("blue", "white", "red"))(20)
# create device
?jpeg
jpeg('correlacion-alumnos-ad.jpg', width=1920, height=1080)
rquery.cormat(dataframe.to.cor, type="full", col=col)
dev.off()
cormat<-rquery.cormat(dataframe.to.cor, type="full", col=col)


```


```{r}
plot.relaciones <- function(x="", y=""){
  alumnos.training$cluster <- clustering.kmeans$cluster
#plot(alumnos.training)
plot(alumnos.training[,c(x, y) ], 
     col = clustering.kmeans$cluster)
}
```

```{r}
plot.relaciones("BECA", "evalucion.socioeconomica")
```

```{r}
plot.relaciones("PDM1", "PSEM1")
```

```{r}
plot.relaciones("VBM1", "PSEM1")
```


```{r}
plot(alumnos.training[,c("VBM1", "PSEM1") ], 
     col = 1:3)
```



















